#ai platform by datastax

[kazifatagar]

DataStax to Launch Massive New AI Platform Updates at RAG++ Event in San Francisco

DataStax is set to unveil significant updates to its AI platform at the RAG++ event in San Francisco, partnering with LangChain, Microsoft, NVIDIA, and others. Key updates include the release of Langflow 1.0, a visual framework for RAG applications now hosted in the DataStax Cloud, and a new partnership with Unstructured.io for efficient data ingestion and preparation for AI use. Read More…

[govindhtech]

NVIDIA NeMo Retriever Microservices Improves LLM Accuracy 

NVIDIA NIM inference microservices

 AI, Get Up! Businesses can unleash the potential of their business data with production-ready NVIDIA NIM inference microservices for retrieval-augmented generation, integrated into the Cohesity, DataStax, NetApp, and Snowflake platforms. The new NVIDIA NeMo Retriever Microservices Boost LLM Accuracy and Throughput.

Applications of generative AI are worthless, or even harmful, without accuracy, and data is the foundation of accuracy.

NVIDIA today unveiled four new NVIDIA NeMo Retriever NIM inference microservices, designed to assist developers in quickly retrieving the best proprietary data to produce informed responses for their  AI applications.

NeMo Retriever NIM microservices, when coupled with the today-announced NVIDIA NIM inference microservices for the Llama 3.1 model collection, allow enterprises to scale to agentic  AI workflow, where AI applications operate accurately with minimal supervision or intervention, while delivering the highest accuracy retrieval-augmented generation, or RAG.

Nemo Retriever

With NeMo Retriever, businesses can easily link bespoke models to a variety of corporate data sources and use RAG to provide AI applications with incredibly accurate results. To put it simply, the production-ready microservices make it possible to construct extremely accurate AI applications by enabling highly accurate information retrieval.

NeMo Retriever, for instance, can increase model throughput and accuracy for developers building AI agents and chatbots for customer support, identifying security flaws, or deriving meaning from intricate supply chain data.

High-performance, user-friendly, enterprise-grade inferencing is made possible by NIM inference microservices. The NeMo Retriever NIM microservices enable developers to leverage all of this while leveraging their data to an even greater extent.

Nvidia Nemo Retriever

These recently released NeMo Retriever microservices for embedding and reranking NIM are now widely accessible:

NV-EmbedQA-E5-v5, a well-liked embedding model from the community that is tailored for text retrieval questions and answers.

Snowflake-Arctic-Embed-L, an optimized community model;

NV-RerankQA-Mistral4B-v3, a popular community base model optimized for text reranking for high-accuracy question answering;

NV-EmbedQA-Mistral7B-v2, a well-liked multilingual community base model fine-tuned for text embedding for correct question answering.

They become a part of the group of NIM microservices that are conveniently available via the NVIDIA API catalogue.

Model Embedding and Reranking

The two model types that make up the NeMo Retriever microservices embedding and reranking have both open and commercial versions that guarantee dependability and transparency.

With the purpose of preserving their meaning and subtleties, an embedding model converts a variety of data types, including text, photos, charts, and video, into numerical vectors that can be kept in a vector database. Compared to conventional large language models, or LLMs, embedding models are quicker and less expensive computationally.

After ingesting data and a query, a reranking model ranks the data based on how relevant it is to the query. These models are slower and more computationally complex than embedding models, but they provide notable improvements in accuracy.Image Credit To Nvidia

NeMo Retriever microservices offers advantages over other options. Developers utilising NeMo Retriever microservices may create a pipeline that guarantees the most accurate and helpful results for their company by employing an embedding NIM to cast a wide net of data to be retrieved, followed by a reranking NIM to cut the results for relevancy.

Developers can create the most accurate text Q&A retrieval pipelines by using the state-of-the-art open, commercial models available with NeMo NIM Retriever. NeMo Retriever microservices produced 30% less erroneous responses for enterprise question answering when compared to alternative solutions.Image Credit To Nvidia

NeMo Retriever microservices Principal Use Cases

NeMo Retriever microservices drives numerous AI applications, ranging from data-driven analytics to RAG and  AI agent solutions.

With the help of NeMo Retriever microservices, intelligent chatbots with precise, context-aware responses can be created. They can assist in the analysis of enormous data sets to find security flaws. They can help glean insights from intricate supply chain data. Among other things, they can improve AI-enabled retail shopping advisors that provide organic, tailored shopping experiences.

For many use cases, NVIDIA AI workflows offer a simple, supported beginning point for creating generative AI-powered products.

NeMo Retriever NIM microservices are being used by dozens of NVIDIA data platform partners to increase the accuracy and throughput of their  AI models.

NIM microservices

With the integration of NeMo Retriever integrating NIM microservices in its Hyper-Converged and Astra DB systems, DataStax is able to provide customers with more rapid time to market with precise, generative AI-enhanced RAG capabilities.

With the integration of NVIDIA NeMo Retriever microservices with Cohesity Gaia, the  AI platform from Cohesity will enable users to leverage their data to drive smart and revolutionary generative AI applications via RAG.

Utilising NVIDIA NeMo Retriever, Kinetica will create LLM agents that can converse naturally with intricate networks in order to react to disruptions or security breaches faster and translate information into prompt action.

In order to link NeMo Retriever microservices to exabytes of data on its intelligent data infrastructure, NetApp and NVIDIA are working together. Without sacrificing data security or privacy, any NetApp ONTAP customer will be able to “talk to their data” in a seamless manner to obtain proprietary business insights.

Services to assist businesses in integrating NeMo Retriever NIM microservices into their  AI pipelines are being developed by NVIDIA’s global system integrator partners, which include Accenture, Deloitte, Infosys, LTTS, Tata Consultancy Services, Tech Mahindra, and Wipro, in addition to their service delivery partners, Data Monsters, EXLService (Ireland) Limited, Latentview, Quantiphi, Slalom, SoftServe, and Tredence.

Nvidia NIM Microservices

Utilize Alongside Other NIM Microservices

NVIDIA Riva NIM microservices, which boost voice  AI applications across industries increasing customer service and enlivening digital humans, can be used with NeMo Retriever microservices.

The record-breaking NVIDIA Parakeet family of automatic speech recognition models, Fastpitch and HiFi-GAN for text-to-speech applications, and Metatron for multilingual neural machine translation are among the new models that will soon be available as Riva NIM microservices.

The modular nature of NVIDIA NIM microservices allows developers to create  AI applications in a variety of ways. To give developers even more freedom, the microservices can be connected with community models, NVIDIA models, or users’ bespoke models in the cloud, on-premises, or in hybrid settings.

Businesses may use NIM to implement  AI apps in production by utilising the NVIDIA AI Enterprise software platform.

NVIDIA-Certified Systems from international server manufacturing partners like Cisco, Dell Technologies, Hewlett Packard Enterprise, Lenovo, and Supermicro, as well as cloud instances from Amazon Web Services, Google Cloud, Microsoft Azure, and Oracle Cloud Infrastructure, can run NIM microservices on customers’ preferred accelerated infrastructure.

Members of the NVIDIA Developer Program will soon have free access to NIM for

Read more on govindhtech.com

[un-enfant-immature]

The 6 most important announcements from Google Cloud Next 2019

Google is hosting its annual Cloud Next developer conference in San Francisco this week. While the event is still in full swing, with a mystery concert capping off most of the programming tonight, the flood of news has now subsided, so here is our list of the most important announcements from the event.

Anthos

What is it? Anthos is the new name of the Google Cloud Services Platform, Google’s managed service for allowing enterprises to run applications in their private data center and in Google’s cloud. Google decided to give the service a new name, Anthos, but also expanded support to AWS and Azure, its competitors’ cloud platforms. This will allow enterprises to use a single platform, running on Google’s cloud, to deploy and manage their applications on any cloud. Enterprises will get a single bill and have a single dashboard to manage their applications. All of this, unsurprisingly, is powered by containers and Kubernetes.

Why does it matter? It’s still highly unusual for the big cloud competitors to launch a product that allows users to run applications on other platforms. The money, after all, is in charging fees for compute time and storage allocations. Google argues that this is something its customers want and that it solves a real problem. Google, however, is also chasing its competitors and looking for ways to differentiate its approach from them. I don’t think we’ll see AWS and Azure react with similar tools, but if they do, it’s a good thing for their customers.

Google’s hybrid cloud platform is coming to AWS and Azure

Open-source integrations into the Google Cloud Console

What is it? Google announced that it would deeply integrate the products of a number of open-source companies into its cloud and essentially make them first-party services. These partners are Confluent, DataStax, Elastic, InfluxData, MongoDB, Neo4j and Redis Labs, with others likely to follow over time.

Why does it matter? These integrations are a boon for Google Cloud customers who are likely already using some of these services. They’ll get a single bill and access to support from these companies, all while managing the services from a single console. The subtext here, though, is a bit more complicated and reveals Google’s approach to open source and puts it into contrast with AWS. Many of the companies that are participating here are highly critical of AWS’s treatment of open source and quite public about it. Google is working with them while the perception is that AWS simply uses the code and doesn’t give back.

Google Cloud challenges AWS with new open-source integrations

Google’s AI Platform

What is it? Google sees its AI prowess as one of its main differentiators in its fight against AWS, Azure and Co. The company already offered a wide range of AI tools, ranging from developer tools and services for advanced data scientists to AutoML, a service that can automatically train models and doesn’t require a PhD. The new AI Platform offers an end-to-end solution for more advanced developers that allows them to go from ingesting data to training and testing their models, to putting them into production. The platform can also use pre-built models.

Why does it matter? AI (and machine learning) is the major focus for all big cloud providers, but the developer experience leaves lots of room for improvement. Having an end-to-end solution is obviously a major step forward here and opens up the promise of machine learning to a wider range of potential users.

Google launches an end-to-end AI platform

Your Android phone is now a security key

What is it? Instead of using a physical security key to enable two-factor authentication, you’ll now be able to use any Android 7+ phone as a security key, too. You set it up in your Google Account and your phone will then use Bluetooth (but without the hassle of creating a Bluetooth connection) to provide your second factor. For now, this only works with Chrome, but Google hopes to turn this into a standard that other browsers and mobile operating system vendors will also support. Google also recommends you still use a regular key as a backup for that inevitable day when you lose your phone.

Why it matters? Two-factor authentication is inherently safer than just using a login and password. Systems that use SMS and push-notifications are still vulnerable to phishing attacks while security keys — and this new Android-based system uses the same standards as existing keys — prevent this by ensuring that you are on a legitimate site. This new system takes the hassle out of using a physical key and may just convince more people to use two-factor authentication.

Google turns your Android phone into a security key

Google Cloud Code

What is it? Cloud Code is a set of plugins and extensions for popular IDE’s like IntelliJ and VS Code. The general idea here is to provide developers with all of the necessary tools to build cloud-native applications — all without having to deal with any of the plumbing work and configuration that comes with that. Using Cloud Code, developers can simply write their applications like before, but then package them as cloud-native apps and ship them to a Kubernetes cluster for testing or production.

Why does it matter? Writing cloud-native apps is complicated and usually involves writing complex configuration files. Cloud Code ideally makes all of this so easy that it’ll be far easier for developers — and the companies that employ them — to make this move to a modern infrastructure.

Google launches Cloud Code to make cloud-native development easier

Google Cloud aims at retailers

What is it? The news here is that Google is launching a vertical solution that’s squarely aimed at retailers. That doesn’t sound all that earth-shattering, does it? But Google Cloud plans to offer more of these specialized solutions over time.

Why does it matter? Google Cloud CEO Thomas Kurian told us that customers are asking for these kinds of integrated solutions that package some of the companies existing tools into integrated solutions that these enterprises can deploy. This is essentially the first time it is doing so (with maybe the exception of healthcare), but it’ll likely offer more of these over time and they could become a major factor in growing the platform’s user base.

Google Cloud takes aim at verticals starting with new set of tools for retailers

Bonus

We also got a chance to sit down with Google Cloud’s new CEO Thomas Kurian to put some of the announcements into context and talk about his vision for Google Cloud going forward.

Google Cloud’s new CEO on gaining customers, startups, supporting open source and more

[morganbelarus]

TechSee nabs $16M for its customer support solution built on computer vision and AR

Chatbots and other AI-based tools have firmly found footing in the world of customer service, used either to augment or completely replace the role of a human responding to questions and complaints, or (sometimes, annoyingly, at the same time as the previous two functions) sell more products to users.

Today, an Israeli startup called TechSee is announcing $16 million in funding to help build out its own twist on that innovation: an AI-based video service, which uses computer vision, augmented reality and a customer’s own smartphone camera to provide tech support to customers, either alongside assistance from live agents, or as part of a standalone customer service “bot.”

Led by Scale Venture Partners — the storied investor that has been behind some of the bigger enterprise plays of the last several years (including Box, Chef, Cloudhealth, DataStax, Demandbase, DocuSign, ExactTarget, HubSpot, JFrog and fellow Israeli AI assistance startup WalkMe), the Series B also includes participation from Planven Investments, OurCrowd, Comdata Group and Salesforce Ventures. (Salesforce was actually announced as a backer in October.)

The funding will be used both to expand the company’s current business as well as move into new product areas like sales.

Eitan Cohen, the CEO and co-founder, said that the company today provides tools to some 15,000 customer service agents and counts companies like Samsung and Vodafone among its customers across verticals like financial services, tech, telecoms and insurance.

The potential opportunity is big: Cohen estimates there are about 2 million customer service agents in the U.S., and about 14 million globally.

TechSee is not disclosing its valuation. It has raised around $23 million to date.

While TechSee provides support for software and apps, its sweet spot up to now has been providing video-based assistance to customers calling with questions about the long tail of hardware out in the world, used for example in a broadband home Wi-Fi service.

In fact, Cohen said he came up with the idea for the service when his parents phoned him up to help them get their cable service back up, and he found himself challenged to do it without being able to see the set-top box to talk them through what to do.

So he thought about all the how-to videos that are on platforms like YouTube and decided there was an opportunity to harness that in a more organised way for the companies providing an increasing array of kit that may never get the vlogger treatment.

“We are trying to bring that YouTube experience for all hardware,” he said in an interview.

The thinking is that this will become a bigger opportunity over time as more services get digitised, the cost of components continues to come down and everything becomes “hardware.”

“Tech may become more of a commodity, but customer service does not,” he added. “Solutions like ours allow companies to provide low-cost technology without having to hire more people to solve issues [that might arise with it.]”

The product today is sold along two main trajectories: assisting customer reps; and providing unmanned video assistance to replace some of the easier and more common questions that get asked.

In cases where live video support is provided, the customer opts in for the service, similar to how she or he might for a support service that “takes over” the device in question to diagnose and try to fix an issue. Here, the camera for the service becomes a customer’s own phone.

Over time, that live assistance is used in two ways that are directly linked to TechSee’s artificial intelligence play. First, it helps to build up TechSee’s larger back catalogue of videos, where all identifying characteristics are removed with the focus solely on the device or problem in question. Second, the experience in the video is also used to build TechSee’s algorithms for future interactions. Cohen said there are now “millions” of media files — images and videos — in the company’s catalogue.

The effectiveness of its system so far has been pretty impressive. TechSee’s customers — the companies running the customer support — say they have on average seen a 40 percent increase in customer satisfaction (NPS scores), a 17 percent decrease in technician dispatches and between 20 and 30 percent increase in first-call resolutions, depending on the industry.

TechSee is not the only company that has built a video-based customer engagement platform: others include Stryng, CallVU and Vee24. And you could imagine companies like Amazon — which is already dabbling in providing advice to customers based on what its Echo Look can see — might be interested in providing such services to users across the millions of products that it sells, as well as provide that as a service to third parties.

According to Cohen, what TechSee has going for it compared to those startups, and also the potential entry of companies like Microsoft or Amazon into the mix, is a head start on raw data and a vision of how it will be used by the startup’s AI to build the business.

“We believe that anyone who wants to build this would have a challenge making it from scratch,” he said. “This is where we have strong content, millions of images, down to specific model numbers, where we can provide assistance and instructions on the spot.”

Salesforce’s interest in the company, he said, is a natural progression of where that data and customer relationship can take a business beyond responsive support into areas like quick warranty verification (for all those times people have neglected to do a product registration), snapping fender benders for insurance claims and of course upselling to other products and services.

“Salesforce sees the synergies between the sales cloud and the service cloud,” Cohen said.

“TechSee recognized the great potential for combining computer vision AI with augmented reality in customer engagement,” said Andy Vitus, partner at Scale Venture Partners, who joins the board with this round. “Electronic devices become more complex with every generation, making their adoption a perennial challenge. TechSee is solving a massive problem for brands with a technology solution that simplifies the customer experience via visual and interactive guidance.”

Original Article : HERE ; This post was curated & posted using : RealSpecific

=> *********************************************** Originally Published Here: TechSee nabs $16M for its customer support solution built on computer vision and AR ************************************ =>

TechSee nabs $16M for its customer support solution built on computer vision and AR was originally posted by Latest news - Feed

[toomanysinks]

The 6 most important announcements from Google Cloud Next 2019

Google is hosting its annual Cloud Next developer conference in San Francisco this week. While the event is still in full swing, with a mystery concert capping off most of the programming tonight, the flood of news has now subsided, so here is our list of the most important announcements from the event.

Anthos

What is it? Anthos is the new name of the Google Cloud Services Platform, Google’s managed service for allowing enterprises to run applications in their private data center and in Google’s cloud. Google decided to give the service a new name, Anthos, but also expanded support to AWS and Azure, its competitors’ cloud platforms. This will allow enterprises to use a single platform, running on Google’s cloud, to deploy and manage their applications on any cloud. Enterprises will get a single bill and have a single dashboard to manage their applications. All of this, unsurprisingly, is powered by containers and Kubernetes.

Why does it matter? It’s still highly unusual for the big cloud competitors to launch a product that allows users to run applications on other platforms. The money, after all, is in charging fees for compute time and storage allocations. Google argues that this is something its customers want and that it solves a real problem. Google, however, is also chasing its competitors and looking for ways to differentiate its approach from them. I don’t think we’ll see AWS and Azure react with similar tools, but if they do, it’s a good thing for their customers.

Google’s hybrid cloud platform is coming to AWS and Azure

Open-source integrations into the Google Cloud Console

What is it? Google announced that it would deeply integrate the products of a number of open-source companies into its cloud and essentially make them first-party services. These partners are Confluent, DataStax, Elastic, InfluxData, MongoDB, Neo4j and Redis Labs, with others likely to follow over time.

Why does it matter? These integrations are a boon for Google Cloud customers who are likely already using some of these services. They’ll get a single bill and access to support from these companies, all while managing the services from a single console. The subtext here, though, is a bit more complicated and reveals Google’s approach to open source and puts it into contrast with AWS. Many of the companies that are participating here are highly critical of AWS’s treatment of open source and quite public about it. Google is working with them while the perception is that AWS simply uses the code and doesn’t give back.

Google Cloud challenges AWS with new open-source integrations

Google’s AI Platform

What is it? Google sees its AI prowess as one of its main differentiators in its fight against AWS, Azure and Co. The company already offered a wide range of AI tools, ranging from developer tools and services for advanced data scientists to AutoML, a service that can automatically train models and doesn’t require a PhD. The new AI Platform offers an end-to-end solution for more advanced developers that allows them to go from ingesting data to training and testing their models, to putting them into production. The platform can also use pre-built models.

Why does it matter? AI (and machine learning) is the major focus for all big cloud providers, but the developer experience leaves lots of room for improvement. Having an end-to-end solution is obviously a major step forward here and opens up the promise of machine learning to a wider range of potential users.

Google launches an end-to-end AI platform

Your Android phone is now a security key

What is it? Instead of using a physical security key to enable two-factor authentication, you’ll now be able to use any Android 7+ phone as a security key, too. You set it up in your Google Account and your phone will then use Bluetooth (but without the hassle of creating a Bluetooth connection) to provide your second factor. For now, this only works with Chrome, but Google hopes to turn this into a standard that other browsers and mobile operating system vendors will also support. Google also recommends you still use a regular key as a backup for that inevitable day when you lose your phone.

Why it matters? Two-factor authentication is inherently safer than just using a login and password. Systems that use SMS and push-notifications are still vulnerable to phishing attacks while security keys — and this new Android-based system uses the same standards as existing keys — prevent this by ensuring that you are on a legitimate site. This new system takes the hassle out of using a physical key and may just convince more people to use two-factor authentication.

Google turns your Android phone into a security key

Google Cloud Code

What is it? Cloud Code is a set of plugins and extensions for popular IDE’s like IntelliJ and VS Code. The general idea here is to provide developers with all of the necessary tools to build cloud-native applications — all without having to deal with any of the plumbing work and configuration that comes with that. Using Cloud Code, developers can simply write their applications like before, but then package them as cloud-native apps and ship them to a Kubernetes cluster for testing or production.

Why does it matter? Writing cloud-native apps is complicated and usually involves writing complex configuration files. Cloud Code ideally makes all of this so easy that it’ll be far easier for developers — and the companies that employ them — to make this move to a modern infrastructure.

Google launches Cloud Code to make cloud-native development easier

Google Cloud aims at retailers

What is it? The news here is that Google is launching a vertical solution that’s squarely aimed at retailers. That doesn’t sound all that earth-shattering, does it? But Google Cloud plans to offer more of these specialized solutions over time.

Why does it matter? Google Cloud CEO Thomas Kurian told us that customers are asking for these kinds of integrated solutions that package some of the companies existing tools into integrated solutions that these enterprises can deploy. This is essentially the first time it is doing so (with maybe the exception of healthcare), but it’ll likely offer more of these over time and they could become a major factor in growing the platform’s user base.

Google Cloud takes aim at verticals starting with new set of tools for retailers

Bonus

We also got a chance to sit down with Google Cloud’s new CEO Thomas Kurian to put some of the announcements into context and talk about his vision for Google Cloud going forward.

Google Cloud’s new CEO on gaining customers, startups, supporting open source and more

  source https://techcrunch.com/2019/04/10/the-6-most-important-announcements-from-google-cloud-next-2019/

[fmservers]

The 6 most important announcements from Google Cloud Next 2019

Google is hosting its annual Cloud Next developer conference in San Francisco this week. While the event is still in full swing, with a mystery concert capping off most of the programming tonight, the flood of news has now subsided, so here is our list of the most important announcements from the event.

Anthos

What is it? Anthos is the new name of the Google Cloud Services Platform, Google’s managed service for allowing enterprises to run applications in their private data center and in Google’s cloud. Google decided to give the service a new name, Anthos, but also expanded support to AWS and Azure, its competitors’ cloud platforms. This will allow enterprises to use a single platform, running on Google’s cloud, to deploy and manage their applications on any cloud. Enterprises will get a single bill and have a single dashboard to manage their applications. All of this, unsurprisingly, is powered by containers and Kubernetes.

Why does it matter? It’s still highly unusual for the big cloud competitors to launch a product that allows users to run applications on other platforms. The money, after all, is in charging fees for compute time and storage allocations. Google argues that this is something its customers want and that it solves a real problem. Google, however, is also chasing its competitors and looking for ways to differentiate its approach from them. I don’t think we’ll see AWS and Azure react with similar tools, but if they do, it’s a good thing for their customers.

Google’s hybrid cloud platform is coming to AWS and Azure

Open-source integrations into the Google Cloud Console

What is it? Google announced that it would deeply integrate the products of a number of open-source companies into its cloud and essentially make them first-party services. These partners are Confluent, DataStax, Elastic, InfluxData, MongoDB, Neo4j and Redis Labs, with others likely to follow over time.

Why does it matter? These integrations are a boon for Google Cloud customers who are likely already using some of these services. They’ll get a single bill and access to support from these companies, all while managing the services from a single console. The subtext here, though, is a bit more complicated and reveals Google’s approach to open source and puts it into contrast with AWS. Many of the companies that are participating here are highly critical of AWS’s treatment of open source and quite public about it. Google is working with them while the perception is that AWS simply uses the code and doesn’t give back.

Google Cloud challenges AWS with new open-source integrations

Google’s AI Platform

What is it? Google sees its AI prowess as one of its main differentiators in its fight against AWS, Azure and Co. The company already offered a wide range of AI tools, ranging from developer tools and services for advanced data scientists to AutoML, a service that can automatically train models and doesn’t require a PhD. The new AI Platform offers an end-to-end solution for more advanced developers that allows them to go from ingesting data to training and testing their models, to putting them into production. The platform can also use pre-built models.

Why does it matter? AI (and machine learning) is the major focus for all big cloud providers, but the developer experience leaves lots of room for improvement. Having an end-to-end solution is obviously a major step forward here and opens up the promise of machine learning to a wider range of potential users.

Google launches an end-to-end AI platform

Your Android phone is now a security key

What is it? Instead of using a physical security key to enable two-factor authentication, you’ll now be able to use any Android 7+ phone as a security key, too. You set it up in your Google Account and your phone will then use Bluetooth (but without the hassle of creating a Bluetooth connection) to provide your second factor. For now, this only works with Chrome, but Google hopes to turn this into a standard that other browsers and mobile operating system vendors will also support. Google also recommends you still use a regular key as a backup for that inevitable day when you lose your phone.

Why it matters? Two-factor authentication is inherently safer than just using a login and password. Systems that use SMS and push-notifications are still vulnerable to phishing attacks while security keys — and this new Android-based system uses the same standards as existing keys — prevent this by ensuring that you are on a legitimate site. This new system takes the hassle out of using a physical key and may just convince more people to use two-factor authentication.

Google turns your Android phone into a security key

Google Cloud Code

What is it? Cloud Code is a set of plugins and extensions for popular IDE’s like IntelliJ and VS Code. The general idea here is to provide developers with all of the necessary tools to build cloud-native applications — all without having to deal with any of the plumbing work and configuration that comes with that. Using Cloud Code, developers can simply write their applications like before, but then package them as cloud-native apps and ship them to a Kubernetes cluster for testing or production.

Why does it matter? Writing cloud-native apps is complicated and usually involves writing complex configuration files. Cloud Code ideally makes all of this so easy that it’ll be far easier for developers — and the companies that employ them — to make this move to a modern infrastructure.

Google launches Cloud Code to make cloud-native development easier

Google Cloud aims at retailers

What is it? The news here is that Google is launching a vertical solution that’s squarely aimed at retailers. That doesn’t sound all that earth-shattering, does it? But Google Cloud plans to offer more of these specialized solutions over time.

Why does it matter? Google Cloud CEO Thomas Kurian told us that customers are asking for these kinds of integrated solutions that package some of the companies existing tools into integrated solutions that these enterprises can deploy. This is essentially the first time it is doing so (with maybe the exception of healthcare), but it’ll likely offer more of these over time and they could become a major factor in growing the platform’s user base.

Google Cloud takes aim at verticals starting with new set of tools for retailers

Bonus

We also got a chance to sit down with Google Cloud’s new CEO Thomas Kurian to put some of the announcements into context and talk about his vision for Google Cloud going forward.

Google Cloud’s new CEO on gaining customers, startups, supporting open source and more

  Via Frederic Lardinois https://techcrunch.com

[computerguideworld-blog]

TechSee nabs $16M for its customer support solution built on computer vision and AR

New Post has been published on https://computerguideto.com/must-see/techsee-nabs-16m-for-its-customer-support-solution-built-on-computer-vision-and-ar/

TechSee nabs $16M for its customer support solution built on computer vision and AR

Chatbots and other AI-based tools have firmly found footing in the world of customer service, used either to augment or completely replace the role of a human responding to questions and complaints, or (sometimes, annoyingly, at the same time as the previous two functions) sell more products to users.

Today, an Israeli startup called TechSee is announcing $16 million in funding to help build out its own twist on that innovation: an AI-based video service, which uses computer vision, augmented reality and a customer’s own smartphone camera to provide tech support to customers, either alongside assistance from live agents, or as part of a standalone customer service “bot.”

Led by Scale Venture Partners — the storied investor that has been behind some of the bigger enterprise plays of the last several years (including Box, Chef, Cloudhealth, DataStax, Demandbase, DocuSign, ExactTarget, HubSpot, JFrog and fellow Israeli AI assistance startup WalkMe), the Series B also includes participation from Planven Investments, OurCrowd, Comdata Group and Salesforce Ventures. (Salesforce was actually announced as a backer in October.)

The funding will be used both to expand the company’s current business as well as move into new product areas like sales.

Eitan Cohen, the CEO and co-founder, said that the company today provides tools to some 15,000 customer service agents and counts companies like Samsung and Vodafone among its customers across verticals like financial services, tech, telecoms and insurance.

The potential opportunity is big: Cohen estimates there are about 2 million customer service agents in the U.S., and about 14 million globally.

TechSee is not disclosing its valuation. It has raised around $23 million to date.

While TechSee provides support for software and apps, its sweet spot up to now has been providing video-based assistance to customers calling with questions about the long tail of hardware out in the world, used for example in a broadband home Wi-Fi service.

In fact, Cohen said he came up with the idea for the service when his parents phoned him up to help them get their cable service back up, and he found himself challenged to do it without being able to see the set-top box to talk them through what to do.

So he thought about all the how-to videos that are on platforms like YouTube and decided there was an opportunity to harness that in a more organised way for the companies providing an increasing array of kit that may never get the vlogger treatment.

“We are trying to bring that YouTube experience for all hardware,” he said in an interview.

The thinking is that this will become a bigger opportunity over time as more services get digitised, the cost of components continues to come down and everything becomes “hardware.”

“Tech may become more of a commodity, but customer service does not,” he added. “Solutions like ours allow companies to provide low-cost technology without having to hire more people to solve issues [that might arise with it.]”

The product today is sold along two main trajectories: assisting customer reps; and providing unmanned video assistance to replace some of the easier and more common questions that get asked.

In cases where live video support is provided, the customer opts in for the service, similar to how she or he might for a support service that “takes over” the device in question to diagnose and try to fix an issue. Here, the camera for the service becomes a customer’s own phone.

Over time, that live assistance is used in two ways that are directly linked to TechSee’s artificial intelligence play. First, it helps to build up TechSee’s larger back catalogue of videos, where all identifying characteristics are removed with the focus solely on the device or problem in question. Second, the experience in the video is also used to build TechSee’s algorithms for future interactions. Cohen said there are now “millions” of media files — images and videos — in the company’s catalogue.

The effectiveness of its system so far has been pretty impressive. TechSee’s customers — the companies running the customer support — say they have on average seen a 40 percent increase in customer satisfaction (NPS scores), a 17 percent decrease in technician dispatches and between 20 and 30 percent increase in first-call resolutions, depending on the industry.

TechSee is not the only company that has built a video-based customer engagement platform: others include Stryng, CallVU and Vee24. And you could imagine companies like Amazon — which is already dabbling in providing advice to customers based on what its Echo Look can see — might be interested in providing such services to users across the millions of products that it sells, as well as provide that as a service to third parties.

According to Cohen, what TechSee has going for it compared to those startups, and also the potential entry of companies like Microsoft or Amazon into the mix, is a head start on raw data and a vision of how it will be used by the startup’s AI to build the business.

“We believe that anyone who wants to build this would have a challenge making it from scratch,” he said. “This is where we have strong content, millions of images, down to specific model numbers, where we can provide assistance and instructions on the spot.”

Salesforce’s interest in the company, he said, is a natural progression of where that data and customer relationship can take a business beyond responsive support into areas like quick warranty verification (for all those times people have neglected to do a product registration), snapping fender benders for insurance claims and of course upselling to other products and services.

“Salesforce sees the synergies between the sales cloud and the service cloud,” Cohen said.

“TechSee recognized the great potential for combining computer vision AI with augmented reality in customer engagement,” said Andy Vitus, partner at Scale Venture Partners, who joins the board with this round. “Electronic devices become more complex with every generation, making their adoption a perennial challenge. TechSee is solving a massive problem for brands with a technology solution that simplifies the customer experience via visual and interactive guidance.”

Read more: https://techcrunch.com

[kazifatagar]

DataStax Enhances GitHub Copilot Extension to Streamline GenAI App Development

DataStax has expanded its GitHub Copilot extension to integrate with its AI Platform-as-a-Service (AI PaaS) solution, aiming to streamline the development of generative AI applications for developers. The enhanced Astra DB extension allows developers to manage databases (vector and serverless) and create Langflow AI flows directly from GitHub Copilot in VS Code using natural language commands.…

[endenogatai]

TechSee nabs $16M for its customer support solution built on computer vision and AR

Chatbots and other AI-based tools have firmly found footing in the world of customer service, used either to augment or completely replace the role of a human responding to questions and complaints, or (sometimes, annoyingly, at the same time as the previous two functions) sell more products to users.

Today, an Israeli startup called TechSee is announcing $16 million in funding to help build out its own twist on that innovation: an AI-based video service, which uses computer vision, augmented reality and a customer’s own smartphone camera to provide tech support to customers, either alongside assistance from live agents, or as part of a standalone customer service ‘bot.’

Led by Scale Venture Partners — the storied investor that has been behind some of the bigger enterprise plays of the last several years (including Box, Chef, Cloudhealth, DataStax, Demandbase, DocuSign, ExactTarget, HubSpot, JFrog, and fellow Israeli AI assistance startup WalkMe) the Series B also includes participation from Planven Investments, OurCrowd, Comdata Group and Salesforce Ventures. (Salesforce was actually announced as a backer in October.)

The funding will be used both to expand the company’s current business as well as move into new product areas like sales.

Eitan Cohen, the CEO and co-founder, said that the company today provides tools to some 15,000 customer service agents and counts companies like Samsung and Vodafone among its customers across verticals like financial services, tech, telecoms and insurance.

The potential opportunity is big: Cohen estimates that there are about 2 million customer service agents in the US, and about 14 million globally.

TechSee is not disclosing its valuation. It has raised around $23 million to date.

While TechSee provides support for software and apps, its sweet spot up to now has been providing video-based assistance to customers calling with questions about the long tail of hardware out in the world, used for example in a broadband home WiFi service.

In fact, Cohen said he came up with the idea for the service when his parents phoned him up to help them get their cable service back up, and he found himself challenged to do it without being able to see the set top box to talk them through what to do.

So he thought about all the how-to videos that are on platforms like YouTube and decided that there was an opportunity to harness that in a more organised way for the companies providing an increasing array of kit that may never get the vlogger treatment.

“We are trying to bring that YouTube experience for all hardware,” he said in an interview.

The thinking is that this will become a bigger opportunity over time as more services get digitised, the cost of components continues to come down and everything becomes “hardware.”

“Tech may become more of a commodity, but customer service does not,” he added. “Solutions like ours allow companies to provide low-cost technology without having to hire more people to solve issues [that might arise with it.]”

The product today is sold along two main trajectories: assisting customer reps; and providing unmanned video assistance to replace some of the easier and more common questions that get asked.

In cases where live video support is provided, the customer opts in for the service, similar to how she or he might for a support service that “takes over” the device in question to diagnose and try to fix an issue. Here, the camera for the service becomes a customer’s own phone.

Over time, that live assistance is used in two ways that are directly linked to TechSee’s artificial intelligence play. First, it helps to build up TechSee’s larger back catalogue of videos, where all identifying characteristics removed with the focus solely on the device or problem in question. Second, the experience in the video is also used to build TechSee’s algorithms for future interactions. Cohen said that there are now “millions” of media files — images and videos — now in the company’s catalogue.

The effectiveness of its system so far has been pretty impressive. TechSee’s customers — the companies running the customer support — say they have on average seen a 40 percent increase in customer satisfaction (NPS scores), a 17 percent decrease in technician dispatches, between 20 and 30 percent increase in first call resolutions, depending on the industry.

TechSee is not the only company that has built a video-based customer engagement platform: others include Stryng, CallVU and Vee24. And you could image companies like Amazon — which is already dabbling in providing advice to customers based on what its Echo Look can see — might be interested in providing such services to users across the millions of products that it sells, as well as provide that as a service to third parties.

According to Cohen, What TechSee has going for it compared to those startups, and also the potential entry of companies like Microsoft or Amazon into the mix, is a headstart on raw data and a vision of how it will be used by the startup’s AI to build the business.

“We believe that anyone who wants to build this would have a challenge making it from scratch,” he said. “This is where we have strong content, millions of images, down to specific model numbers, where we can provide assistance and instructions on the spot.”

Salesforce’s interest in the company, he said, is a natural progression of where that data and customer relationship can take a business beyond responsive support into areas like quick warranty verification (for all those times people have neglected to do a product registration), snapping fender benders for insurance claims, and of course upselling to other products and services.

“Salesforce sees the synergies between the sales cloud and the service cloud,” Cohen said.

“TechSee recognized the great potential for combining computer vision AI with augmented reality in customer engagement,” said Andy Vitus, Partner at Scale Venture Partners, who joins the board with this round. “Electronic devices become more complex with every generation, making their adoption a perennial challenge. TechSee is solving a massive problem for brands with a technology solution that simplifies the customer experience via visual and interactive guidance.”

from RSSMix.com Mix ID 8204425 https://ift.tt/2QKi57z via IFTTT

[theinvinciblenoob]

Chatbots and other AI-based tools have firmly found footing in the world of customer service, used either to augment or completely replace the role of a human responding to questions and complaints, or (sometimes, annoyingly, at the same time as the previous two functions) sell more products to users.

Today, an Israeli startup called TechSee is announcing $16 million in funding to help build out its own twist on that innovation: an AI-based video service, which uses computer vision, augmented reality and a customer’s own smartphone camera to provide tech support to customers, either alongside assistance from live agents, or as part of a standalone customer service ‘bot.’

Led by Scale Venture Partners — the storied investor that has been behind some of the bigger enterprise plays of the last several years (including Box, Chef, Cloudhealth, DataStax, Demandbase, DocuSign, ExactTarget, HubSpot, JFrog, and fellow Israeli AI assistance startup WalkMe) the Series B also includes participation from Planven Investments, OurCrowd, Comdata Group and Salesforce Ventures. (Salesforce was actually announced as a backer in October.)

The funding will be used both to expand the company’s current business as well as move into new product areas like sales.

Eitan Cohen, the CEO and co-founder, said that the company today provides tools to some 15,000 customer service agents and counts companies like Samsung and Vodafone among its customers across verticals like financial services, tech, telecoms and insurance.

The potential opportunity is big: Cohen estimates that there are about 2 million customer service agents in the US, and about 14 million globally.

TechSee is not disclosing its valuation. It has raised around $23 million to date.

While TechSee provides support for software and apps, its sweet spot up to now has been providing video-based assistance to customers calling with questions about the long tail of hardware out in the world, used for example in a broadband home WiFi service.

In fact, Cohen said he came up with the idea for the service when his parents phoned him up to help them get their cable service back up, and he found himself challenged to do it without being able to see the set top box to talk them through what to do.

So he thought about all the how-to videos that are on platforms like YouTube and decided that there was an opportunity to harness that in a more organised way for the companies providing an increasing array of kit that may never get the vlogger treatment.

“We are trying to bring that YouTube experience for all hardware,” he said in an interview.

The thinking is that this will become a bigger opportunity over time as more services get digitised, the cost of components continues to come down and everything becomes “hardware.”

“Tech may become more of a commodity, but customer service does not,” he added. “Solutions like ours allow companies to provide low-cost technology without having to hire more people to solve issues [that might arise with it.]”

The product today is sold along two main trajectories: assisting customer reps; and providing unmanned video assistance to replace some of the easier and more common questions that get asked.

In cases where live video support is provided, the customer opts in for the service, similar to how she or he might for a support service that “takes over” the device in question to diagnose and try to fix an issue. Here, the camera for the service becomes a customer’s own phone.

Over time, that live assistance is used in two ways that are directly linked to TechSee’s artificial intelligence play. First, it helps to build up TechSee’s larger back catalogue of videos, where all identifying characteristics removed with the focus solely on the device or problem in question. Second, the experience in the video is also used to build TechSee’s algorithms for future interactions. Cohen said that there are now “millions” of media files — images and videos — now in the company’s catalogue.

The effectiveness of its system so far has been pretty impressive. TechSee’s customers — the companies running the customer support — say they have on average seen a 40 percent increase in customer satisfaction (NPS scores), a 17 percent decrease in technician dispatches, between 20 and 30 percent increase in first call resolutions, depending on the industry.

TechSee is not the only company that has built a video-based customer engagement platform: others include Stryng, CallVU and Vee24. And you could image companies like Amazon — which is already dabbling in providing advice to customers based on what its Echo Look can see — might be interested in providing such services to users across the millions of products that it sells, as well as provide that as a service to third parties.

According to Cohen, What TechSee has going for it compared to those startups, and also the potential entry of companies like Microsoft or Amazon into the mix, is a headstart on raw data and a vision of how it will be used by the startup’s AI to build the business.

“We believe that anyone who wants to build this would have a challenge making it from scratch,” he said. “This is where we have strong content, millions of images, down to specific model numbers, where we can provide assistance and instructions on the spot.”

Salesforce’s interest in the company, he said, is a natural progression of where that data and customer relationship can take a business beyond responsive support into areas like quick warranty verification (for all those times people have neglected to do a product registration), snapping fender benders for insurance claims, and of course upselling to other products and services.

“Salesforce sees the synergies between the sales cloud and the service cloud,” Cohen said.

“TechSee recognized the great potential for combining computer vision AI with augmented reality in customer engagement,” said Andy Vitus, Partner at Scale Venture Partners, who joins the board with this round. “Electronic devices become more complex with every generation, making their adoption a perennial challenge. TechSee is solving a massive problem for brands with a technology solution that simplifies the customer experience via visual and interactive guidance.”

via TechCrunch

[martechadvisor-blog]

3 Challenges with Big Data for Marketers

Is big data really the ideal solution for marketers? We explore three very real roadblocks to marketers embracing big data and why we feel less mature marketers may turn towards other customer data management solutions instead

There is no doubt that big data – the huge volume of data flowing from various sources in a steady – and high-speed – stream has the potential to change the way businesses can leverage data to find their competitive edge. 

While marketers too understand the potential of big data to transform their understanding of the customer, unfortunately, they also realize that big data is not a magical remedy for them. For starters, marketers are not professionally trained data scientists; progressive technologies such as the ones that drive big data are usually well beyond a marketer’s capabilities, and the control of big data still lies with technology people, who may or may not understand the business outcomes functions like marketing are chasing. Wore, because big data is – well, big – and encompasses all the data from all the sources that belong to the business –data relevant to marketing is in very real danger of getting lost in the deluge. 

Hence, even though big data is now commonplace – even the smallest of companies can accurately claim to be generating big data in this always-on era – a lot of marketers don't feel adequately prepared to start using it as a tool to drive their CX efforts. 

Martech Advisor tracked the relationship between marketing and big data and we are forced to admit that we see 3 big practical problems when it comes to leveraging big data as a realistic or practical resource for improved marketing decision making: 

Here are 3 of the most glaring roadblocks to marketing making the most of big data: 

1. Collaborating with the IT team to use big data: this is the first stumbling block to using big data. It needs complex technologies (you’ve probably heard of something called Hadoop) that can structure and transform the data into a format that makes it usable. Remember, big data is a torrent of data flowing into the company from multiple sources - including logistics, manufacturing, billing, customer service, etc. Now, while there is merit in juxtaposing all that information about a customer’s experience on top of all the marketing related information that comes in from purely marketing systems, there is the very real danger of the torrent turning into a deluge. Marketers with limited resources may prefer to start with the pure marketing related data - flowing in from multiple marketing systems. Collaborating with IT, who is the custodian of organization-wide big data, and being dependent on them to get the insights they need is a least appealing option to marketers. It’s exactly why CDP and DMP companies are thriving - they are giving control of customer data to marketers, without needing any additional technical skills.

2. Integrating and translating big data points into useful insight: using any data optimally is a challenge for all business leader, and marketers are no different. Marketers are still developing their data analysis skills, just with the data generated by the marketing systems. As Neil Michel, Chief Strategy Officer at Wire Stone says in this article about how marketers struggle with data big and small, "in developing data analysis capabilities, marketers certainly need help. Many modern marketing tools do a great job of producing single-channel reports but understanding the contributions – and opportunities – across channels requires integrating data." Like we just pointed out, the real problem faced by marketers in the pursuit of data-driven marketing strategies has nothing to do with the availability of data. In fact, many suffer from the problem of data deluge. Instead, it is the ability to bring together multiple sources of data, connect the dots to turn the data points into actionable insights and then to quickly execute based on those insights before you drown. 

Luckily, technology can now help. As Kevin Cunningham, Chief Executive Officer, MRP says in his piece about big data for B2B marketers, "AI-enabled analytics platforms provide the best customer acquisition and retention success by automating much of the decision making around the signals generated from all of this data, allowing the marketing organization to focus more on strategy and less on tactical execution." While these technologies surely will help marketers ‘ask the right questions’ or identify the right patterns, what is in doubt is whether marketers will choose to apply this to a large data set like big data or turn to data unifying solutions such as CDPs or simpler analytics tools built into their martech stack components to get the insights they?

3. How much speed do we really need? **One defining characteristic of big data is velocity. It's like a river of data flowing through the pipes**. As Karl Van den Bergh, Chief Marketing Officer, Datastax argues in this piece; marketers struggle to engender value from big data primarily because the value of data is directly proportional to the speed as well as the extent to which it can be employed. Marketers need real-time, creative, and customer-responsive apps and tools to ensure they are able to deliver the right messages at the right time from the insights gleaned. Speed is crucial, and so is the agility to act with speed. However, as the volume of big data grows, the time available to make decisions is shrinking. Turning marketing data into insight is much easier and faster, and the analytical tools and context to understand marketing data are already available. 

There is no doubt that to really have a holistic view of the customers' interactions with the brand; big data is as important as the marketing data. However, **addressing the challenges of knowing what data to gather when there is a deluge; which tools to use to understand the data, and how to take the insights thus garnered to market – at the speed and customization needed – are still big gray areas for marketers**. Two years ago, when CDPs were not half as prevalent as they are today, big data held out great hope. But today, as marketers seek to take back control of the customer database, one has to wonder if big data is really as relevant to marketers as it may once have seemed. 

Read the top 10 best performing articles about data-driven marketing on MarTech Advisor here What are your experiences or views? Share your thoughts here!

This article was first appeared on MarTech Advisor

[un-enfant-immature]

TechSee nabs $16M for its customer support solution built on computer vision and AR

Chatbots and other AI-based tools have firmly found footing in the world of customer service, used either to augment or completely replace the role of a human responding to questions and complaints, or (sometimes, annoyingly, at the same time as the previous two functions) sell more products to users.

Today, an Israeli startup called TechSee is announcing $16 million in funding to help build out its own twist on that innovation: an AI-based video service, which uses computer vision, augmented reality and a customer’s own smartphone camera to provide tech support to customers, either alongside assistance from live agents, or as part of a standalone customer service ‘bot.’

Led by Scale Venture Partners — the storied investor that has been behind some of the bigger enterprise plays of the last several years (including Box, Chef, Cloudhealth, DataStax, Demandbase, DocuSign, ExactTarget, HubSpot, JFrog, and fellow Israeli AI assistance startup WalkMe) the Series B also includes participation from Planven Investments, OurCrowd, Comdata Group and Salesforce Ventures. (Salesforce was actually announced as a backer in October.)

The funding will be used both to expand the company’s current business as well as move into new product areas like sales.

Eitan Cohen, the CEO and co-founder, said that the company today provides tools to some 15,000 customer service agents and counts companies like Samsung and Vodafone among its customers across verticals like financial services, tech, telecoms and insurance.

The potential opportunity is big: Cohen estimates that there are about 2 million customer service agents in the US, and about 14 million globally.

TechSee is not disclosing its valuation. It has raised around $23 million to date.

While TechSee provides support for software and apps, its sweet spot up to now has been providing video-based assistance to customers calling with questions about the long tail of hardware out in the world, used for example in a broadband home WiFi service.

In fact, Cohen said he came up with the idea for the service when his parents phoned him up to help them get their cable service back up, and he found himself challenged to do it without being able to see the set top box to talk them through what to do.

So he thought about all the how-to videos that are on platforms like YouTube and decided that there was an opportunity to harness that in a more organised way for the companies providing an increasing array of kit that may never get the vlogger treatment.

“We are trying to bring that YouTube experience for all hardware,” he said in an interview.

The thinking is that this will become a bigger opportunity over time as more services get digitised, the cost of components continues to come down and everything becomes “hardware.”

“Tech may become more of a commodity, but customer service does not,” he added. “Solutions like ours allow companies to provide low-cost technology without having to hire more people to solve issues [that might arise with it.]”

The product today is sold along two main trajectories: assisting customer reps; and providing unmanned video assistance to replace some of the easier and more common questions that get asked.

In cases where live video support is provided, the customer opts in for the service, similar to how she or he might for a support service that “takes over” the device in question to diagnose and try to fix an issue. Here, the camera for the service becomes a customer’s own phone.

Over time, that live assistance is used in two ways that are directly linked to TechSee’s artificial intelligence play. First, it helps to build up TechSee’s larger back catalogue of videos, where all identifying characteristics removed with the focus solely on the device or problem in question. Second, the experience in the video is also used to build TechSee’s algorithms for future interactions. Cohen said that there are now “millions” of media files — images and videos — now in the company’s catalogue.

The effectiveness of its system so far has been pretty impressive. TechSee’s customers — the companies running the customer support — say they have on average seen a 40 percent increase in customer satisfaction (NPS scores), a 17 percent decrease in technician dispatches, between 20 and 30 percent increase in first call resolutions, depending on the industry.

TechSee is not the only company that has built a video-based customer engagement platform: others include Stryng, CallVU and Vee24. And you could image companies like Amazon — which is already dabbling in providing advice to customers based on what its Echo Look can see — might be interested in providing such services to users across the millions of products that it sells, as well as provide that as a service to third parties.

According to Cohen, What TechSee has going for it compared to those startups, and also the potential entry of companies like Microsoft or Amazon into the mix, is a headstart on raw data and a vision of how it will be used by the startup’s AI to build the business.

“We believe that anyone who wants to build this would have a challenge making it from scratch,” he said. “This is where we have strong content, millions of images, down to specific model numbers, where we can provide assistance and instructions on the spot.”

Salesforce’s interest in the company, he said, is a natural progression of where that data and customer relationship can take a business beyond responsive support into areas like quick warranty verification (for all those times people have neglected to do a product registration), snapping fender benders for insurance claims, and of course upselling to other products and services.

“Salesforce sees the synergies between the sales cloud and the service cloud,” Cohen said.

“TechSee recognized the great potential for combining computer vision AI with augmented reality in customer engagement,” said Andy Vitus, Partner at Scale Venture Partners, who joins the board with this round. “Electronic devices become more complex with every generation, making their adoption a perennial challenge. TechSee is solving a massive problem for brands with a technology solution that simplifies the customer experience via visual and interactive guidance.”

[kazifatagar]

DataStax Enhances AI Platform with Glean and Unstructured Integrations at RAG++ Event in NYC

Malaysia, 11 September 2024 – DataStax, a leading AI platform company, has announced significant updates to its AI PaaS at the RAG++ event in New York City. The platform now offers enhanced features to improve data relevancy, response times, and throughput for developers working on AI applications. With industry partners such as Glean, Unstructured.io, and NVIDIA, DataStax showcased its latest…

[martechadvisor-blog]

3 Challenges with Big Data for Marketers

Is big data really the ideal solution for marketers? We explore three very real roadblocks to marketers embracing big data and why we feel less mature marketers may turn towards other customer data management solutions instead

There is no doubt that big data – the huge volume of data flowing from various sources in a steady – and high-speed – stream has the potential to change the way businesses can leverage data to find their competitive edge. 

While marketers too understand the potential of big data to transform their understanding of the customer, unfortunately, they also realize that big data is not a magical remedy for them. For starters, marketers are not professionally trained data scientists; progressive technologies such as the ones that drive big data are usually well beyond a marketer’s capabilities, and the control of big data still lies with technology people, who may or may not understand the business outcomes functions like marketing are chasing. Wore, because big data is – well, big – and encompasses all the data from all the sources that belong to the business –data relevant to marketing is in very real danger of getting lost in the deluge. 

Hence, even though big data is now commonplace – even the smallest of companies can accurately claim to be generating big data in this always-on era – a lot of marketers don't feel adequately prepared to start using it as a tool to drive their CX efforts. 

Martech Advisor tracked the relationship between marketing and big data and we are forced to admit that we see 3 big practical problems when it comes to leveraging big data as a realistic or practical resource for improved marketing decision making: 

Here are 3 of the most glaring roadblocks to marketing making the most of big data: 

1. Collaborating with the IT team to use big data: this is the first stumbling block to using big data. It needs complex technologies (you’ve probably heard of something called Hadoop) that can structure and transform the data into a format that makes it usable. Remember, big data is a torrent of data flowing into the company from multiple sources - including logistics, manufacturing, billing, customer service, etc. Now, while there is merit in juxtaposing all that information about a customer’s experience on top of all the marketing related information that comes in from purely marketing systems, there is the very real danger of the torrent turning into a deluge. Marketers with limited resources may prefer to start with the pure marketing related data - flowing in from multiple marketing systems. Collaborating with IT, who is the custodian of organization-wide big data, and being dependent on them to get the insights they need is a least appealing option to marketers. It’s exactly why CDP and DMP companies are thriving - they are giving control of customer data to marketers, without needing any additional technical skills.

2. Integrating and translating big data points into useful insight: using any data optimally is a challenge for all business leader, and marketers are no different. Marketers are still developing their data analysis skills, just with the data generated by the marketing systems. As Neil Michel, Chief Strategy Officer at Wire Stone says in this article about how marketers struggle with data big and small, "in developing data analysis capabilities, marketers certainly need help. Many modern marketing tools do a great job of producing single-channel reports but understanding the contributions – and opportunities – across channels requires integrating data." Like we just pointed out, the real problem faced by marketers in the pursuit of data-driven marketing strategies has nothing to do with the availability of data. In fact, many suffer from the problem of data deluge. Instead, it is the ability to bring together multiple sources of data, connect the dots to turn the data points into actionable insights and then to quickly execute based on those insights before you drown. 

Luckily, technology can now help. As Kevin Cunningham, Chief Executive Officer, MRP says in his piece about big data for B2B marketers, "AI-enabled analytics platforms provide the best customer acquisition and retention success by automating much of the decision making around the signals generated from all of this data, allowing the marketing organization to focus more on strategy and less on tactical execution." While these technologies surely will help marketers ‘ask the right questions’ or identify the right patterns, what is in doubt is whether marketers will choose to apply this to a large data set like big data or turn to data unifying solutions such as CDPs or simpler analytics tools built into their martech stack components to get the insights they?

3. How much speed do we really need? **One defining characteristic of big data is velocity. It's like a river of data flowing through the pipes**. As Karl Van den Bergh, Chief Marketing Officer, Datastax argues in this piece; marketers struggle to engender value from big data primarily because the value of data is directly proportional to the speed as well as the extent to which it can be employed. Marketers need real-time, creative, and customer-responsive apps and tools to ensure they are able to deliver the right messages at the right time from the insights gleaned. Speed is crucial, and so is the agility to act with speed. However, as the volume of big data grows, the time available to make decisions is shrinking. Turning marketing data into insight is much easier and faster, and the analytical tools and context to understand marketing data are already available. 

There is no doubt that to really have a holistic view of the customers' interactions with the brand; big data is as important as the marketing data. However, **addressing the challenges of knowing what data to gather when there is a deluge; which tools to use to understand the data, and how to take the insights thus garnered to market – at the speed and customization needed – are still big gray areas for marketers**. Two years ago, when CDPs were not half as prevalent as they are today, big data held out great hope. But today, as marketers seek to take back control of the customer database, one has to wonder if big data is really as relevant to marketers as it may once have seemed. 

Read the top 10 best performing articles about data-driven marketing on MarTech Advisor here What are your experiences or views? Share your thoughts here!

This article was first appeared on MarTech Advisor