Generative AI continues to develop at a blistering pace, making it more important than ever that organizations have access to enterprise-ready capabilities to help them leverage this disruptive technology. 

Harnessing the power of decades of Google’s research, innovation, and investment in AI, Google Cloud continues to make generative AI available with baked-in security, data governance, and scalability across the board. 

To this end, last month, we announced the general availability of Generative AI support on Vertex AI, giving our customers the ability to access powerful foundation models from Google Research and tools for customizing and applying them. 

Today we are announcing the general availability (GA) of four important foundation models for Vertex AI. These include Imagen, PaLM 2 for Chat, Codey, and Chirp. For each of these models, organizations can access APIs on Model Garden and do prompt design and tuning on Generative AI Studio.

• Imagen includes four key features:
  • Image generation for creating studio-grade images at scale
  • Image editing to edit generated or existing images via text prompts 
  • Image captioning for creating captions of images at scale
  • Visual Question & Answering (VQA) for interacting with, analyzing, and explaining images
• PaLM 2 for Chat follows the general availability of PaLM 2 for Text in June 
• Codey supports code generation, completion, and code chat
• Chirp supports multilingual Speech AI 

We’re also announcing Multimodal Embeddings API in preview, which lets customers combine the power of Vertex AI’s generative AI models with their proprietary data, to generate embeddings, or interchangeable vector representations, of their text and image data. These capabilities can enable data science teams to deliver a variety of downstream tasks such as image classification, content recommendations, and visual search. 

In this blog post, we’ll explore what your organization can do with these powerful models and how Vertex AI provides the enterprise-ready capabilities you can use to get up and running with generative AI. 

Helping to drive enterprise value from Generative AI models

Powerful models are the foundation of generative AI, but the software, tools, and infrastructure that surround these models are equally important for enterprise adoption. Organizations face challenges not only accessing these models, but also integrating AI while maintaining protection over intellectual property, adhering to regulations around data security and privacy, and ensuring models and applications are safe to use. Many organizations also want to use generative AI without incurring large costs or managing huge clusters.

We help address these challenges head-on with Vertex AI’s platform capabilities for scalable application integration, purpose-built AI infrastructure, secure and private data customization, and responsible use of this technology. 

Let’s see how each of these pillars can help your organization. 

Access models to build production-ready generative applications 
Vertex AI can make it easy to access foundation models, as today’s model announcements attest. While models are an inextricable part of generative AI, the software that helps enterprises use this technology is equally important—which is why Vertex AI also offers a range of tools for tuning, deploying, monitoring, and maintaining models, so you can build differentiated applications using your own data. 

Turning to today’s announcements, in May we announced Imagen, our foundation model for image generation. Now, we are excited to announce Imagen is generally available with an allowlist (i.e., approved access via your sales representative), letting onboarded customers start using image generation and editing capabilities. Visual Q&A and Captioning for production workloads are also generally available for all customers. Visual Q&A provides new ways to engage with image-based data like retail products or image libraries. This new capability can give you answers to questions about an image, helping you analyze large amounts of data quickly, and it can even help the visually impaired understand images or graphs that they wouldn’t be able to otherwise. Captioning, meanwhile, can make it easy to generate relevant descriptions for your images. Captions can help with indexing and searching, as well as assigning image descriptions to product listings on eCommerce websites. 

“Imagen is beginning to power key capabilities within Omni, Omnicom’s open operating system, that will enable 17,000+ trained and certified users to create audience-driven customized images in minutes. Imagen has been instrumental in offering a scalable platform for image generation and customization. Integrating it into our platform allows us to expand the scope of audience-powered creative inspiration, at a scale that wasn’t previously possible,” said Art Schram, Annalect Chief Product Officer at Omnicom. “We’re starting to adopt the latest features like styles and fine tuning, and engineering data-driven prompts. We look forward to continuing to provide our users relevant visual inspiration in a responsible way.”

“The latest improvements in Imagen’s product preservation capabilities are a perfect match for Typeface’s focus on personalized AI for brands,” explained Vishal Sood, Head of Product at Typeface. “By combining Google Vertex AI’s Imagen with Typeface’s brand-personalized AI, we are able to help enterprises to create 10x personalized content in a fraction of time.”

Google Shopping recently built an application called Product Studio using Imagen on Vertex AI. Product Studio can enable merchants to create rich product images quickly and easily, at a fraction of the time it takes to do professional product photo shoots. “We’re excited about the feedback we’re getting from merchants in our early pilots, who say that Product Studio, which leverages Imagen on Vertex AI, helps them generate and publish lifestyle product photos directly to their product catalogs,” says Jeff Harrell, Google’s Senior Director of Product Management for Merchant Shopping. 

Announced in May, PaLM 2 is a family of models that power dozens of Google products, including Bard and Duet AI in Google Cloud. With the PaLM 2 for Chat model, now generally available, you can leverage Google’s PaLM’s variety of abilities for multi-turn chat applications, such as shopping assistants, customer support agents, and more. 

ThoughtSpot, provider of a widely-adopted business intelligence platform, is using PaLM 2 to build a new feature in ThoughtSpot for Google Sheets called “AI Explain,” which can instantly generate explanations of charts, visuals, and anomalies, and will launch new conversational AI and ML-enabled predictive forecasting capabilities into its analytics platform.

With Codey, your organization’s developers can accelerate a wide variety of coding tasks, helping to empower them to work efficiently and close skills gaps. The model enables not only code completion and code generation capabilities, but also chat to help with debugging, documentation, learning new concepts, and more. Since launching in preview in May, we’ve added additional programming languages including Go, Google Standard SQL, Java, Javascript, Python, and Typescript. We’ve also improved the quality of code responses and increased serving capacity, enabling your developers with the right tools to enter the era of generative engineering.  

“Security and privacy are key to incorporating AI into the software development lifecycle,” said David DeSanto, Chief Product Officer at GitLab. “GitLab leverages Vertex AI to deliver new, AI-powered features with a privacy-first approach, including the ability to run our own models and leverage Codey foundation models built on top of PaLM 2. The GitLab DevSecOps platform empowers organizations to harness the benefits of AI for faster software delivery, while ensuring their data, intellectual property, and source code are protected.”

Originally released in May in preview, Chirp is a version of our 2 billion-parameter speech model, which was trained on millions of hours of audio and supports over 100 languages. Chirp achieves 98% accuracy on English and relative improvement of up to 300% in languages with less than 10 million speakers. Whether the use case involves customer support, transcriptions, or voice control, Chirp can help your organization communicate with customers and constituents inclusively, by engaging audiences in their native languages. 

Last but not least, our Multimodal Embeddings API, now in preview, can unlock an array of new applications, such as image and text-based recommendations, by enabling the processing of text and images interchangeably. This capability complements our Text Embeddings API, which became generally available in June, and remains a recommended choice for those with fully text-based use cases. Multimodal Embeddings API makes it possible to categorize images and text together and can be crucial for use cases like retail recommendation systems that can provide relevant outputs from both images of products and text descriptions.

Match generative AI with infrastructure 
Beyond access to models and tools for building generative AI apps, you need infrastructure to make sure your apps can scale and reliably perform — ideally without running into daunting compute costs or management overhead that distracts your technical talent from building innovative products. Google Cloud offers the choice and power to run smaller models running finite tasks at the lowest latency levels, as well as to run large models capable of cutting-edge experiments. 

As our large language model customers are looking to scale up their projects and applications using our models, they often need assurances that their requests will be serviced with acceptable performance. This is especially critical for delivering real-time applications where customer service is paramount. Starting in August, Vertex AI will support provisioned, dedicated generative AI capacity that can deliver guaranteed throughput. This feature can be especially beneficial to customers who have a high volume of sustained workloads.  

Leverage generative AI while protecting data and privacy 
One capability enabled by Google Cloud is the ability to customize models using your own data. Vertex AI can help customers keep their data protected, secure, and private. When a company tunes a foundation model in Vertex AI, private data, model outputs, and prompts can be kept private, and they are never used in the foundation model training corpus. We recently published a whitepaper, “Adaptation of Large Foundation Models,” which outlines how we help protect customer data. 

Auditability and compliance are essential to helping ensure the security and privacy of customer data. We also engage in comprehensive GDPR privacy efforts, including our transparency commitments for customer data usage and the support for our customer’s Data Protection Impact Assessments (DPIAs). Now, we’re excited to support HIPAA compliance for many of our generally available models on Vertex AI, so that healthcare and life science customers with whom we have a Business Associate Agreement can run workloads with Protected Health Information (PHI) data on Google Cloud. 

Innovate responsibly 
Our AI Principles put beneficial use, user safety, and avoidance of harms above business outcomes and are embedded in how we develop our AI products. We’ve conducted extensive reviews on our generative AI products to identify potential risks and have developed guardrails to mitigate these impacts. For example, to address concerns around safety, we’ve implemented safety filters for bias, toxicity, and other harmful content. We also equip our customers with the tools they need to help reduce risk within their applications and provide recommendations to help navigate responsible AI. 

Bring the power of generative AI to your organization

With both a wide selection of foundation models and extensive, enterprise-grade platform capabilities, Vertex AI continues to unlock ways for your business or organization to access foundation models, tune them on your proprietary data, and leverage them for differentiated apps and digital experiences. To take the next step, visit our product page or reach out to our sales representatives to gain access to our latest capabilities.

Gartner® named Google as a Leader in the 2022 Magic Quadrant™ for Cloud AI Developer Services report. This evaluation covered Google’s language, vision and structured data products including AutoML, all of which we deliver through Google Cloud. We believe this recognition is a reflection of the confidence and satisfaction that customers have in our language, vision, and AutoML products for developers. Google remains a Leader for the third year in a row, based upon the completeness of our vision and our ability to execute.

Developers benefit in many ways by using Cloud AI services and solutions. Customers recognize the advantages of Google’s AI and ML services for developers, such as Vertex AI, BigQuery ML, AutoML and AI APIs. In addition, customers benefit from the pace of progress in the field of Responsible AI and actionable ethics processes applied to all customer and partner solutions leveraging Google Cloud technology, as well as our core architecture including the Vertex AI platform, vision, conversational AI, language and structured data, and optimization services and key vertical industry solutions.

We believe that our ‘Leader’ placement validates this vision for AI developer tools. Let’s take a closer look at some of the report findings.

ML tools purpose-built for developers

Google’s machine learning tools have been built by developers, for developers, based on the groundbreaking research generated from Google Research and DeepMind. This developer empathy drives product development, which supports the developer community to achieve deep value from Google’s AI and ML services. An example of this is the unification of all of the tools needed for building, deploying and managing ML models into one ML platform, Vertex AI, resulting in accelerated time to production. They also cite BigQuery ML, AutoML for language, vision video and tabular data) and prebuilt ML APIs (such as speech and translation) as having high utility for developers at all levels of ML expertise to build custom AI and quickly infuse AI into their applications.

Leading organizations like OTOY, Allen Institute for AI and DeepMind (an Alphabet subsidiary) choose Google for ML, and enterprises like Twitter, Wayfair and The Home Depot shared more about their partnership with Google in their recent sessions at Google Next 2021.

Responsible AI principles and practices

Responsible AI is a critical component of successful AI. A 2020 study commissioned by Google Cloud and the Economic Intelligence Unit highlighted that ethical AI does not only prevent organizations from making egregious mistakes, but that the value of responsible AI practices for competitive edge, as well as talent acquisition and retention are notable. At Google, we not only apply our ethics review process to first party platforms and solutions, to ensure that our services design-in responsible AI from the outset, we also consult with customers and partners based on AI principles to deliver accountability and avoid unfair biases. In addition, our best-in-class tools provide developers with the functionality they need to evaluate fairness and biases in datasets and models. Our Explainable AI tools such as model cards provide model transparency in a structured, accessible way, and the What-If Tool is essential for developers and data scientists to evaluate, debug and improve their ML models.

Clear and understandable product architecture

Google Cloud’s investment in our ML product portfolio has led to a comprehensive, integrated and open offering that spans breadth (across vision, conversational AI, language and structured data, and optimization services) and depth (core AI services, with features such as Vertex AI Pipelines and Vertex Explainable AI built on top). Industry-specific solutions tailored by Google for retail, financial services, manufacturing, media and healthcare customers, such as Recommendations AI, Visual Inspection AI, Media Translation, Healthcare Data Engine, add another layer leveraging this foundational platform to help organizations and users adopt machine learning solutions more easily.

At Google Cloud, we refuse to make developers jump through hoops to derive value out of our technology; instead, we bring the value directly to them by ensuring that all of our AI and ML products and solutions work seamlessly together. To download the full report, click here. Get started on Vertex AI and talk with our sales team.

Disclaimer:

Gartner, Magic Quadrant for Cloud AI Developer Services, Van Baker, Arun Batchu, Erick Brethenoux, Svetlana Sicular, Mike Fang, May 23, 2022.

Gartner and Magic Quadrant are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

As one of the most innate and ubiquitous forms of expression, speech is a fundamental pillar of human interaction. It comes as no surprise, then, that Google Cloud’s Speech API  has become a crucial tool for enterprise customers, launched to general availability (GA) over six years ago and, now, processing over 1 billion voice minutes each month.

With the Speech API, we’ve been pleased to serve thousands of customers and provide industry-leading speech recognition quality and cost-effective products across a range of industries. We want to constantly evolve our offerings and bring new benefits to organizations, which is why today, we’re excited to announce the GA release of our new Speech-to-Text v2 API. 

Speech-to-Text v2 modernizes our API interface and introduces several new features. It also migrates all of our existing functionality, so you can use the same models and features that you were using in STT v1 or v1p1beta1 APIs. This new version of our API also allows us to take advantage of significant cost savings in our serving path, and as such we are reducing our base price, as well as adding pricing incentives for large workloads and those willing to accept longer turnaround times.

This new infrastructure also allows us to serve a wide variety of new types of models, including Chirp , our latest 2B-parameter large speech model. All of these are Generally Available to Google Cloud Platform customers and users starting today.

Let’s have a more thorough look though at the enhanced features of Speech-to-Text API V2 and illustrate how your business can benefit from our new capabilities:

Expanding Speech-to-Text features with V2 API

Since the official launch of Speech-to-Text API back in 2017, we’ve utilized Google’s global infrastructure to host and monitor our production-facing transcription models. This robust, well-connected network has been the backbone of our offering for all of our customers. 

However, a unified view of our Speech-to-Text service has been a crucial request for our enterprise customers  who need to satisfy data residency and compliance requirements, especially in regulated industries like banking and public sector. We listened carefully to this feedback, and starting today, our Speech-to-Text v2 API supports full regionalization, allowing our customers to invoke identical copies of all our transcription models in the Google Cloud Platform region of their choice. 

In addition to giving users the flexibility to deploy in any region, we are adding a number of new features to help developers build on the API: 

• Recognizers: A user-defined named configuration that combines a model identifier, the language-locale of the audio to be transcribed, and the cloud region for the transcription model to run. Once-created, the recognizer can be referenced to every subsequent transcription request, eliminating the need for users to repeatedly define the same configuration parameters. This resourceful implementation of recognizers allows for greater flexibility in authentication and authorization, as users are not longer required to set up dedicated service accounts.
• Cloud Logging: Requests performed using a recognizer object automatically support cloud logging by default. Since the recognizers are defined as named entities, customers can partition traffic based on the recognizer of interest or collectively. 
• Audio Format Auto-Detection: Instead of having our users analyze and manually define the audio configuration settings to pass in a transcription request, the new Speech-to-Text V2 API detects settings like encoding, sampling rate, and channel count, then automatically populates the request configuration parameters. 

Increasing accuracy at Enterprise Scale with Chirp

As part of our continuous investment in foundational speech models, in March 2023 we released research results for our Universal Speech Mode (USM), a family of state-of-the-art speech models with 2B parameters and support for transcriptions of 300+ languages. In May 2023, at Google I/O, we announced Chirp in Private Preview, the latest version of the USM family, fine-tuned for our Cloud-specific use-cases. 

Chirp is now GA through the Speech-to-Text v2 API. Following extensive testing and feedback from our customers, we are making the power of pre-trained large models accessible through a simple enterprise-grade API surface. Our early adopters have seen major strides in customer engagement, thanks to the market-leading accuracy and language coverage of the new model, and we cannot wait to see what opportunities our enterprise customers will unlock.

Introducing new pricing, tiers, and options

We have heard from customers that price can be just as important as quality for many workloads. That’s why the Speech-to-Text API v2 features totally new pricing. We have lowered the cost of real-time and batch transcription from $0.024 per minute to $0.016 per minute. Additionally, we know that pricing can be a concern for those that have very large transcription workloads. For that reason, we are also introducing standard volume tiers, allowing costs as low as $0.004 per minute. As always, additional discounts are available to those with even larger workloads.

Staying true to our commitment on flexibility and choice in pricing, we noticed that even though the majority of our customers are interested in our models for real-time transcription scenarios, many are also interested in non-real time transcriptions for data at rest. With our new API v2 infrastructure, we can take greater advantage of capacity that goes unused at certain times. With our new Dynamic Batch pricing, we are passing this savings on to customers that are less latency sensitive. Aimed at users who can wait up to 24 hours for transcription results, Dynamic Batch is a new discounted pricing tier that offers 75% lower price per minute for transcription audio relative to our Standard tier.

For more information on Dynamic Batch and all our new STT v2 API pricing, check out our pricing page.

Learn more and start you Speech-to-Text journey

For more information to help get you started on your migration journey from V1 to V2, head over to the detailed documentation and try our walkthroughs to make the most of regionalization and recognizers.

If you are curious to learn more about how to use Chirp, you can get  started by following our tutorial. You can also read more about how we built it in our research blog post.

We are very excited to bring all these improvements to our developer ecosystem and provide you with the tools to leverage the power of voice for your businesses, programs, and applications.

Patients about to undergo a healthcare procedure understandably want the best medical professionals they can get. But how can they know which doctors have had the most experience and the best outcomes with that particular procedure? How can they make an informed decision about which doctor to select when the information they have is limited to the doctor’s practice area and subjective reviews from other patients?

MD Insider is working to solve that problem using machine learning (ML) to objectively analyze doctor performance. By analyzing data from thousands of institutions and millions of doctor-patient interactions and medical events, MD Insider identifies physician performance insights based on their experience and outcomes. Insights are then integrated into a triage engine, that enables consumers to search for and schedule appointments with providers who meet their clinical criteria and convenience preferences, such as insurances accepted, office hours, locations, and language.

MD Insider also offers robust data APIs to help health systems, health plans, and employers reduce costs and improve quality of care. Payers use the APIs to curate high-quality provider networks and manage provider directories. Examples of MD Insider’s data APIs include Provider Experience and Share of Practice metrics, Provider Quality and Outcomes, Network Modeling, Expert Clinical Search Taxonomy, Find a Provider, Acute-Care Hospital Quality, and Provider and Facility Metadata.

“We use Google Cloud Platform the way the cloud was supposed to be used. By comparison, other cloud providers feel like you’re renting somebody else’s data center.”

—Ed Holsinger, Lead Data Engineer and Head of Data Science, MD Insider

MD Insider is continuously ingesting the latest performance data about physicians and analyzing billions of rows of data. Requiring constant scalability, the company was born in the cloud; however, it had difficulty configuring server instances for the optimal balance of memory and CPU, and its Hadoop cluster had to be kept running 24/7. Network performance was often slow for no apparent reason. As a result, failure rates from node timeouts increased, and costs grew along with the data. MD Insider had to estimate its usage and pay up front, and received little financial benefit from sustained use commitments.

Knowing that data would continue to grow, MD Insider decided to move its data services — the most demanding and complex portion of its infrastructure — to Google Cloud Platform (GCP), and took advantage of GCP managed services for container management and big data analytics.

“One of the reasons we decided to move to Google Cloud Platform is because it feels like a unified, well-designed cloud architecture and pricing model,” says Ed Holsinger, Lead Data Engineer and Head of Data Science at MD Insider. “We use Google Cloud Platform the way the cloud was supposed to be used. By comparison, other cloud providers feel like you’re renting somebody else’s data center.”

“Moving to Google Cloud Platform and using Kubernetes Engine gave us 5x the processing power for analyzing physician performance at 75% less cost. Our data scientists have more power than ever before to generate insights for our customers.”

—Ed Holsinger, Lead Data Engineer and Head of Data Science, MD Insider

5x the performance, 75% less cost

MD Insider now uses Kubernetes Engine to automate container management and deploy clusters in minutes with just a few clicks. When hundreds of machines are required to analyze a large dataset, automation in Kubernetes Engine deploys ML models as containers, each of which manages the full lifecycle of its task, including scaling up resources, deploying results, and scaling back down when the task is finished. It’s easy for MD Insider to specify exactly how much CPU and memory each container needs, helping maximize performance while reducing costs.

“Moving to Google Cloud Platform and Kubernetes Engine gave us 5x the processing power for analyzing physician performance at 75% less cost,” says Ed. “Our data scientists have more power than ever before to generate insights for our customers. Data scoring jobs that used to take three business days now take four hours.”

Adds Galen Meurer, Senior Software Engineer at MD Insider: “Even if all Google Cloud Platform had to offer was Kubernetes Engine, I would still want to use it. Previously we spent up to 30% of our time managing our container infrastructure, which we can now use for product development.”

A foundation for data science

MD Insider was happy to find that GCP offers a wide variety of managed services. For example, the company is supplementing its Kubernetes Engine clusters with BigQuery for its big data masters and selection jobs, enabling scientists to analyze new and different types of data as well as analyze larger datasets in less time. MD Insider also uses Cloud Storage for big data staging and Cloud Dataproc to run managed Apache Spark clusters for data processing.

“Google Cloud Platform gives us an incredibly powerful cloud architecture for data engineering and data science,” says Eric Wilson, CEO of MD Insider. “That gives our scientists independence, they can do what they need to do without waiting and with no contention between them.”

A developer-friendly platform

Migrating its data services was such a success that MD Insider decided to move the rest of its infrastructure to GCP, including the front end for its web application. Since the migration, MD Insider has experienced no unplanned downtime on GCP, allowing it to easily meet the 99.5% uptime SLA it promises to customers. It’s also taking advantage of Build Triggers in Kubernetes Engine to automate container builds and reduce build times by more than 40%. Production code can be updated in seconds, with no impact to end users other than making new features available.

“GCP has simplified our workflow in so many ways, from intelligent load balancing to content delivery and automating builds,” says Matthew Frey, Software Engineer. “Everything on GCP is cohesive and developer friendly, with a superior UI and better network performance than other cloud providers.”

Ryan Beaini, Senior Software Engineer at MD Insider, agrees: “Since we moved to GCP, our developers are definitely happier. The pain and the headaches we experienced because of the limitations of our previous toolset all went away.”

“We’re a small company, but what we’re doing is incredibly important. We’re helping people make decisions about healthcare providers that could impact their lives and even be life-saving. Google Cloud Platform is helping us make our mission bigger, better, and brighter.”

—Eric Wilson, CEO, MD Insider

Securing billions of rows of clinical and non-clinical healthcare data

As a healthcare technology company, MD Insider processes billions of rows of clinical and non-clinical healthcare data. To control user access to GCP, it uses Identity & Access Management (IAM) along with Yubico YubiKeys for hardware-based two-factor authentication when logging into Google Workspace. MD Insider takes comfort that GCP encrypts data at rest by default, and encrypts and authenticates data in transit when data moves outside physical boundaries not controlled by Google or on behalf of Google.

“On GCP, everything that we need to be encrypted for compliance purposes is encrypted, which is fantastic,” says Eric. “When I tell our potential clients and partners about the resources that Google has dedicated to security, it gives them the confidence that their data will be protected.”

Transforming how teams work

As a growing company, MD Insider must collaborate seamlessly between offices in California, Colorado, and Illinois. It relies on Google Workspace for communication and productivity, using Docs, Sheets, and Slides to drive the business. Employee and team files are stored in Drive, and meetings are conducted via Google Meet with Chromebox for Meetings videoconferencing hardware kits. Google Workspace also helps MD Insider maintain information security by authenticating email domains with digital signatures in Gmail and scanning outgoing email using Gmail Data Loss Prevention (DLP).

“I use Google Workspace every day, and everyone else here does too,” says Eric. “Team Drives are a big time saver for us. We’ve let our previous office software expire, because there’s no need to pay for those licenses anymore.”

Promoting healthcare transparency

With GCP helping MD Insider increase velocity and momentum, the company is making exciting progress. For example, it has entered into a strategic partnership with Zelis Healthcare, which will use MD Insider’s API to provide insights for a next-gen analytics platform that will give health plans unprecedented transparency around physician performance.

“We’re a small company, but what we’re doing is incredibly important,” says Eric. “We’re helping people make decisions about healthcare providers that could impact their lives and even be life-saving. Google Cloud Platform is helping us make our mission bigger, better, and brighter.”

*Google Workspace was formerly known as G Suite prior to Oct. 6, 2020.