Recently, Google Cloud partner Groovenauts, Inc. published a live demo of MatchIt Fast. As the demo shows, you can find images and text similar to a selected sample from a collection of millions in a matter of milliseconds:

Give it a try — and either select a preset image or upload one of your own. Once you make your choice, you will get the top 25 similar images from two million images on Wikimedia images in an instant, as you can see in the video above. No caching involved.

The demo also lets you perform the similarity search with news articles. Just copy and paste some paragraphs from any news article, and get similar articles from 2.7 million articles on the GDELT project within a second.

Vector Search: the technology behind Google Search, YouTube, Play, and more

How can it find matches that fast? The trick is that the MatchIt Fast demo uses the vector similarity search (or nearest neighbor search or simply vector search) capabilities of the Vertex AI Matching Engine, which shares the same backend as Google Image Search, YouTube, Google Play, and more, for billions of recommendations and information retrievals for Google users worldwide. The technology is one of the most important components of Google’s core services, and not just for Google: it is becoming a vital component of many popular web services that rely on content search and information retrieval accelerated by the power of deep neural networks.

So what’s the difference between traditional keyword-based search and vector similarity search? For many years, relational databases and full-text search engines have been the foundation of information retrieval in modern IT systems. For example, you would add tags or category keywords such as “movie”, “music”, or “actor” to each piece of content (image or text) or each entity (a product, user, IoT device, or anything really). You’d then add those records to a database, so you could perform searches with those tags or keywords.

In contrast, vector search uses vectors (where each vector is a list of numbers) for representing and searching content. The combination of the numbers defines similarity to specific topics. For example, if an image (or any content) includes 10% of “movie”, 2% of “music”, and 30% of “actor”-related content, then you could define a vector [0.1, 0.02, 0.3] to represent it. (Note: this is an overly simplified explanation of the concept; the actual vectors have much more complex vector spaces). You can find similar content by comparing the distances and similarities between vectors. This is how Google services find valuable content for a wide variety of users worldwide in milliseconds.

With keyword search, you can only specify a binary choice as an attribute of each piece of content; it’s either about a movie or not, either music or not, and so on. Also, you cannot express the actual “meaning” of the content to search. If you specify a keyword “films”, for example, you would not see any content related to “movies” unless there was a synonyms dictionary that explicitly linked these two terms in the database or search engine. 

Vector search provides a much more refined way to find content, with subtle nuances and meanings. Vectors can represent a subset of content that contains “much about actors, some about movies, and a little about music”. Vectors can represent the meaning of content where “films”, “movies”, and “cinema” are all collected together. Also, vectors have the flexibility to represent categories  previously unknown to or undefined by service providers. For example, emerging categories of content primarily attractive to kids, such as ASMR or slime, are really hard for adults or marketing professionals to predict beforehand, and going back through vast databases to manually update content with these new labels would be all but impossible to do quickly. But vectors can capture and represent never-before-seen categories instantly.

Vector search changes business

Vector search is not only applicable to image and text content. It can also be used for information retrieval for anything you have in your business when you can define a vector to represent each thing. Here are a few examples:

• Finding similar users: If you define a vector to represent each user in your business by combining the user’s activities, past purchase history, and other user attributes, then you can find all users similar to a specified user.  You can then see, for example, users who are purchasing similar products, users that are likely bots, or users who are potential premium customers and who should be targeted with digital marketing.
• Finding similar products or items: With a vector generated from product features such as description, price, sales location, and so on, you can find similar products to answer any number of questions; for example, “What other products do we have that are similar to this one and may work for the same use case?” or “What products sold in the last 24 hours in this area?” (based on time and proximity)
• Finding defective IoT devices: With a vector that captures the features of defective devices from their signals, vector search enables you to instantly find potentially defective devices for proactive maintenance.
• Finding ads: Well-defined vectors let you find the most relevant or appropriate ads for viewers in milliseconds at high throughput.
• Finding security threats: You can identify security threats by vectorizing the signatures of computer virus binaries or malicious attack behaviors against web services or network equipment. 
• …and many more: Thousands of different applications of vector search in all industries will likely emerge in the next few years, making the technology as important as relational databases.

OK, vector search sounds cool. But what are the major challenges to applying the technology to real business use cases? Actually there are two:

• Creating vectors that are meaningful for business use cases
• Building a fast and scalable vector search service

Embeddings: meaningful vectors for business use cases

The first challenge is creating vectors for representing various entities that are meaningful and useful for business use cases. This is where deep learning technology can really shine. In the case of the MatchIt Fast demo, the application simply uses a pre-trained MobileNet v2 model for extracting vectors from images, and the Universal Sentence Encoder (USE) for text. By applying such models to raw data, you can extract “embeddings” – vectors that map each row of data in a space of their “meanings”. MobileNet puts images that have similar patterns and textures closer to one another in the embedding space, and USE puts texts that have similar topics closer.

For example, a carefully designed and trained machine learning model could map movies into an embedding space like the following:

With the embedding space shown here, users could find recommended movies based on the two dimensions: is the movie for children or adults, and is it a blockbuster or arthouse movie? This is a very simple example, of course, but with an embedding space like this that fits your business requirements, you can deliver a better user experience on recommendation and information retrieval services with insights extracted from the model. 

For more about creating embeddings, the Machine Learning Crash Course on Recommendation Systems is a great way to get started. We will also discuss how to extract better embeddings from business data later in this post.

Building a fast and scalable vector search service

Suppose that you have successfully extracted useful vectors (embeddings) from your business data. Now the only thing you have to do is search for similar vectors. That sounds simple, but in practice it is not. Let’s see how the vector search works when you implement it with BigQuery in a naive way:https://www.youtube.com/embed/wHNJspvxj2w?enablejsapi=1&

It takes about 20 seconds to find similar items (fish images in this case) from a pool of one million items. That level of performance is not so impressive, especially when compared to the MatchIt Fast demo. BigQuery is one of the fastest data warehouse services in the industry, so why does the vector search take so long?

This illustrates the second challenge: building a fast and scalable vector search engine isn’t an easy task. The most widely used metrics for calculating the similarity between vectors are L2 distance (Euclidean distance), cosine similarity, and inner product (dot product).

But all require calculations proportional to the number of vectors multiplied by the number of dimensions if you implement them in a naive way. For example, if you compare a vector with 1024 elements to 1M vectors, the number of calculations will be proportional to 1024 x 1M = 1.02B. This is the computation required to look through all the entities for a single search, and the reason why the BigQuery demo above takes so long.

Instead of comparing vectors one by one, you could use the approximate nearest neighbor (ANN) approach to improve search times. Many ANN algorithms use vector quantization (VQ), in which you split the vector space into multiple groups, define “codewords” to represent each group, and search only for those codewords. This VQ technique dramatically enhances query speeds and is the essential part of many ANN algorithms, just like indexing is the essential part of relational databases and full-text search engines.

As you may be able to conclude from the diagram above, as the number of groups in the space increases the speed of the search decreases and the accuracy increases.  Managing this trade-off — getting higher accuracy at shorter latency — has been a key challenge with ANN algorithms. 

Last year, Google Research announced ScaNN, a new solution that provides state-of-the-art results for this challenge. With ScaNN, they introduced a new VQ algorithm called anisotropic vector quantization:

Anisotropic vector quantization uses a new loss function to train a model for VQ for an optimal grouping to capture farther data points (i.e. higher inner product) in a single group. With this idea, the new algorithm gives you higher accuracy at lower latency, as you can see in the benchmark result below (the violet line): 

This is the magic ingredient in the user experience you feel when you are using Google Image Search, YouTube, Google Play, and many other services that rely on recommendations and search. In short, Google’s ANN technology enables users to find valuable information in milliseconds, in the vast sea of web content.

How to use Vertex AI Matching Engine

Now you can use the same search technology that powers Google services with your own business data. Vertex AI Matching Engine is the product that shares the same ScaNN based backend with Google services for fast and scalable vector search, and recently it became GA and ready for production use. In addition to ScaNN, Matching Engine gives you additional features as a commercial product, including:

• Scalability and availability: The open source version of ScaNN is a good choice for evaluation purposes, but as with most new and advanced technologies, you can expect challenges when putting it into production on your own. For example, how do you operate it on multiple nodes with high scalability, availability, and maintainability? Matching Engine uses Google’s production backend for ScaNN, which provides auto-scaling and auto-failover with a large worker pool. It is capable of handling tens of thousands of requests per second, and returns search results in less than 10 ms for the 90th percentile with a recall rate of 95 – 98%.
• Fully managed: You don’t have to worry about building and maintaining the search service. Just create or update an index with your vectors, and you will have a production-ready ANN service deployed. No need to think about rebuilding and optimizing indexes, or other maintenance tasks.
• Filtering: Matching Engine provides filtering functionality that enables you to filter search results based on tags you specify on each vector. For example, you can assign “country” and “stocked” tags to each fashion item vector, and specify filters like “(US OR Canada) AND stocked”  or “not Japan AND stocked” on your searches.

Let’s see how to use Matching Engine with code examples from the MatchIt Fast demo.

Generating embeddings

Before starting the search, you need to generate embeddings for each item like this one:

  {
  "Id":"b5c65fea9b0b8a57bfa574ea",
  "Embedding": [
    0.16329009830951691,
    0.92436742782592773,
    0.00095699273515492678,
    0.011479727923870087,
    0.0089491046965122223,
    0.019959751516580582,
    0.031516745686531067,
    0.0066015380434691906,
    0.46404418349266052,
    ...

This is an embedding with 1280 dimensions for a single image, generated with a MobileNet v2 model. The MatchIt Fast demo generates embeddings for two million images with the following code:

  class Vectorizer:
    def __init__(self):
        self._model = tf.keras.Sequential([hub.KerasLayer("https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/5", trainable=False)])
        self._model.build([None, 224, 224, 3])  # Batch input shape.

    def vectorize(self, jpeg_file):
        ...snip...
        embedding = self._model.predict({"inputs": input_tensor})[0].tolist()
        return embedding

After you generate the embeddings, you store them in a Google Cloud Storage bucket. 

Configuring an index

Then, define a JSON file for the index configuration:

  {
  "contentsDeltaUri": "gs://match-it-fast-us-central1/wikimedia_images/index-1",
  "config": {
    "dimensions": 1280,
    "approximateNeighborsCount": 150,
    "distanceMeasureType": "SQUARED_L2_DISTANCE",
    "algorithm_config": {
      "treeAhConfig": {
        "leafNodeEmbeddingCount": 1000,
        "leafNodesToSearchPercent": 5
      }
    }
  }
}

You can find a detailed description for each field in the documentation, but here are some important fields:

• contentsDeltaUri: the place where you have stored the embeddings
• dimensions: how many dimensions in the embeddings
• approximateNeighborsCount: the default number of neighbors to find via approximate search 
• distanceMeasureType: how the similarity between embeddings should be measured, either L1, L2, cosine or dot product (this page explains which one to choose for different embeddings)

To create an index on the Matching Engine, run the following gcloud command where the metadata-file option takes the JSON file name defined above.

  gcloud --project=gn-match-it-fast beta ai indexes create \
       --display-name=wikimedia-images \
       --description="Wikimedia Image Demo" \
       --metadata-file=metadata/wikimedia_images_index_metadata.json \
       --region=us-central1

Run the search

Now the Matching Engine is ready to run. The demo processes each search request in the following order:

1. First, the web UI takes an image (the one chosen or uploaded by the user) and encodes it into an embedding using the TensorFlow.js MobileNet v2 model running inside the browser. Note: this “client-side encoding” is an interesting option for reducing network traffic when you can run the encoding at the client. In many other cases, you would encode contents to embeddings with a server-side prediction service such as Vertex AI Prediction, or just retrieve pre-generated embeddings from a repository like Vertex AI Feature Store.
2. The App Engine frontend receives the embedding and submits a query to the Matching Engine. Note that you can also use any other compute services in Google Cloud for submitting queries to Matching Engine, such as Cloud Run, Compute Engine, or Kubernetes Engine, or whatever is most suitable for your applications.
3. Matching Engine executes its search. The connection between App Engine and Matching Engine is provided via a VPC private network for optimal latency.
4. Matching Engine returns the IDs of similar vectors in its index.

Step 3 is implemented with the following code:

  class MatchingQueryClient:
    ...snip...

    def query_embedding(self, embedding, num_neighbors=30):
        request = match_service_pb2.MatchRequest()
        request.deployed_index_id = self._deployed_index_id
        for v in embedding:
            request.float_val.append(v)
        request.num_neighbors = num_neighbors
        response = self._stub.Match(request)
        return response

The request to the Matching Engine is sent via gRPC as you can see in the code above. After it gets the request object, it specifies the index id, appends elements of the embedding, specifies the number of neighbors (similar embeddings) to retrieve, and calls the Match function to send the request. The response is received within milliseconds.

Next steps: Making changes for various use cases and better search quality

As we noted earlier, the major challenges in applying vector search on production use cases are:

• Creating vectors that are meaningful for business use cases
• Building a fast and scalable vector search service

From the example above, you can see that Vertex AI Matching Engine solves the second challenge. What about the first one? Matching Engine is a vector search service; it doesn’t include the creating vectors part.

The MatchIt Fast demo uses a simple way of extracting embeddings from images and contents; specifically it uses an existing pre-trained model (either MobileNet v2 or Universal Sentence Encoder). While those are easy to get started with, you may want to explore other options to generate embeddings for other use cases and better search quality, based on your business and user experience requirements.

For example, how do you generate embeddings for product recommendations?  The Recommendation Systems section of the Machine Learning Crash Course is a great resource for learning how to use collaborative filtering and DNN models (the two-tower model) to generate embeddings for recommendation. Also, TensorFlow Recommenders provides useful guides and tutorials for the topic, especially on the two-tower model and advanced topics. For integration with Matching Engine, you may also want to check out the Train embeddings by using the two-tower built-in algorithm page.

Another interesting solution is the Swivel model. Swivel is a method for generating item embeddings from an item co-occurrence matrix. For structured data, such as purchase orders, the co-occurrence matrix of items can be computed by counting the number of purchase orders that contain both product A and product B, for all products you want to generate embeddings for. To learn more, take a look at this tutorial on how to use the model with Matching Engine.

If you are looking for more ways to achieve better search quality, consider metric learning, which enables you to train a model for discrimination between entities in the embedding space, not only classification:

Popular pre-trained models such as the MobileNet v2 can classify each object in an image, but they are not explicitly trained to discriminate the objects from each other with a defined distance metric. With metric learning, you can expect better search quality by designing the embedding space optimized for various business use cases. TensorFlow Similarity could be an option for integrating metric learning with Matching Engine.

Interested? Today, we’re just beginning the migration from traditional search technology to new vector search. Over the next 5 to 10 years, many more best practices and tools will be developed in the industry and community. These tools and best practices will help answer many questions, like… How do you design your own embedding space for a specific business use case? How do you measure search quality? How do you debug and troubleshoot the vector search? How do you build a hybrid setup with existing search engines for meeting sophisticated requirements? There are many new challenges and opportunities ahead for introducing the technology to production. Now’s the time to get started delivering better user experiences and seizing new business opportunities with Matching Engine powered by vector search.

Acknowledgements

We would like to thank Anand Iyer, Phillip Sun, and Jeremy Wortz for their invaluable feedback to this post.

We’re excited to announce that Google Cloud Next returns on October 11–13, 2022. 

Join us for keynotes from industry luminaries and engage live with Google developers. Explore dynamic content across various learning levels, and dive deep into technologies and solutions spanning the Google Cloud and Google Workspace portfolios. Participate in breakout sessions, demos, and hands-on training. Hear from the world’s leading companies about their digital transformation journeys. You’ll have opportunities to connect with experts, get inspired, and boost your skills. We can’t wait to see you at Next ’22!

It’s too early to determine how the event experience will span the digital and physical worlds, so please stay tuned for updates as we plan with the health and safety of the attendees in mind. In the meantime, mark October 11–13 in your calendar, and visit our event site for updates. For more inspiration, rediscover Next ’21, now available on demand.

Contact Center AI (CCAI) brings Google’s innovation in conversational AI to solve the most challenging customer service needs while lowering operational costs. More than a thousand customers have deployed CCAI and are steadily turning it on to power their production contact centers.

Today, we’re excited to announce that we’ve made CCAI even stronger with Agent Assist for Chat, now in public preview.

Agent Assist provides your human agents with continuous support during their calls and now chats by identifying the customers’ intent and providing them with real-time recommendations such as articles and FAQs as well as responses to customer messages to more effectively resolve the conversation.

Customers using Agent Assist for Chat have been able to manage up to 28% more conversations concurrently, while also driving up customer satisfaction by 10%. Additionally, we’ve seen them respond up to 15% faster to chats, reducing chat abandonment rates and solving more customer problems.

Agent Assist provides two key components to help agents manage conversations better: 

• Smart Reply provides response suggestions to agents so they can quickly and appropriately respond to customer messages. These suggestions can be taken from your top performing agents as well as modified even further to ensure suggestions properly reflect the tone and voice of your brand. Agent Assist learns when and what recommendations to make by building a custom model that’s trained on your (and only your) data.
• Knowledge Assist unlocks the power of your knowledge base to provide articles and FAQ suggestions to agents in real-time as the conversation progresses. When using Knowledge Assist, agents no longer need to make the customer wait while they navigate multiple applications and data to find the resolution to the customer’s issue — the answer is delivered right to them.  

“We’ve been very impressed by the chat capabilities of Agent Assist,” said Chris Smith, Vice President of Digital Service at Optus, one of the largest telecommunications companies in Australia

Optus has been using CCAI Dialogflow CX to send queries to virtual agents and sees great potential to use Agent Assist to provide recommendations to their customer support representatives. They expect Agent Assist to help minimize repetitive tasks by providing response and typeahead suggestions, helping improve the efficiency of their agents and the quality and consistency of service they provide.

Another customer, LoveHolidays, is using Agent Assist to support their agents and customers in the travel industry. 

“Agent Assist has been a beneficial aid to agents and our customers alike… It gives us the power to flex our contact center staff levels in hours not weeks,” said Eugene Neale, Director of CX Engineering & Business IT at LoveHolidays

Analysts say online chat is becoming one of the most popular ways to reach out to businesses for customer support. IDC research finds that single-function contact centers worldwide are increasingly rare — in 2020, although phone/voice is still responsible for most interactions (at around 18%); email is responsible for around 13% of interactions, and live chat (without automation) is responsible for around 8% of interactions, according to IDC, Toward the AI-Powered Contact Center, Doc # EUR147017320, December 2020.

Deploying CCAI with Agent Assist for Chat

As part of Google’s Contact Center AI suite, Agent Assist provides a seamless handoff from chats managed by your Dialogflow CX virtual agents. If a conversation or customer requires a live agent, Agent Assist will help your team pick it up quickly and drive it to a satisfying resolution. 

Historically, when managers saw contact center volumes increase they had two choices: allow customers to wait longer to speak to someone (lowering customer satisfaction) or bring on more agents (increasing cost to serve).  Deploying CCAI provides contact center leaders with a third choice: equip agents with tools like, Agent Assist for Chat, to efficiently manage customer interactions while maintaining high quality service.

Global CCAI partners support Agent Assist for Chat

Agent Assist for Chat is a set of public APIs that your engineering team can integrate directly into an agent desktop to control the agent experience from end-to-end. For a more out-of-the-box solution, we have partnered with LivePerson and 247.ai to build Agent Assist directly into their agent desktops.

“Integrating our Conversational Cloud directly with Agent Assist means agents can leverage cutting-edge productivity AI to build even further on the massive ROI of conversational commerce, from reduced agent effort and time-to-respond to increased customer satisfaction and revenue,” said Alex Spinelli, CTO of LivePerson. 

More Agent Assist resources

To learn more, check out the Agent Assist webpage. Give Agent Assist a try by training a model and then testing it using the Agent Assist simulator.

Editor’s note: Prevision.io has built the first ever pay-as-you-go AI management platform that simplifies the machine learning project lifecycle while offering powerful analytics capabilities. Now available exclusively on Google Cloud Marketplace, users can experiment, build, deploy, and manage AI projects in the cloud in weeks—without having extensive data science knowledge.

Similar to the momentum that cloud technology has had in the business world, AI and machine learning are quickly becoming essential to the enterprise. 86% of companies now view AI as a “mainstream technology,” and corporate AI adoption rose 50% in 2021 from the year prior for initiatives such as service-operations optimization and product enhancement. But for companies that don’t fall into the Fortune 500, initiating AI projects can come with several challenges, ranging from one-size-fits-all subscription models to skills gaps and resource-heavy monitoring requirements.

My team of data scientists saw a real need for software that could democratize machine learning innovation by removing these common barriers. We knew that features like automation could make building and deploying AI much more doable for other data scientists, as well as citizen data scientists. So, we launched Prevision.io, a first-of-its-kind dedicated AI management platform built on Google Cloud and now available exclusively on Google Cloud Marketplace.

As a Google Cloud Partner Advantage Member, we knew the benefits of an elastic infrastructure, full integration with BigQuery, and access to a large library of complementary tools, such as Kubeflow on Google Cloud. As a result, it’s easier for our users to improve upon their robust AI projects.

Set up in minutes, deploy a machine learning model in weeks

Users can start building and deploying models in Prevision.io immediately after subscribing through Google Cloud Marketplace. Instead of taking weeks to onboard and months to launch a real-world model into production, Prevision.io’s intuitive interface and powerful predictive analytics capability makes it possible to set up in minutes–and have models up and running in three to four weeks. Since we believe in cost-efficient scaling, our platform operates on a pay-as-you-go model with no long-term contracts, licensing, or per-user fees.

Simply connect Prevision.io with your data–whether it exists in buckets or in an SQL data source like BigQuery. No matter your data source, set up is quick. There are even tutorials to help if you’re using APIs. Follow this tutorial for step-by-step set up details in Google Cloud.

Once historical data is imported, you can start applying your own models inside Prevision.io, or use Prevision.io to build a bespoke model. There’s more good news: expenditures on Prevision.io’s platform are applied toward customers’ Google Cloud spend commitments.

Full lifecycle AI project management made easy

By automating the complexity of AI project management with a no-code approach, businesses do not have to add more data scientists to their teams, risk data drift or outdated models, spend hundreds of thousands on unused software, or massively expand IT budgets. By connecting BigQuery or other datastores to Prevision.io, you can launch high-performing machine learning projects and manage them across the entire lifecycle. With Prevision.io you can:

• Experiment with iteration and optimization to get an effective model into production from the start. Track performance and compare versions to identify the most reliable model. Because there is no infrastructure to manage, users can focus only on the project and see ROI sooner.
• Automate training and prediction tasks to improve collaboration, reduce time-consuming manual operations, and boost results. Users can implement automation across the production pipeline with built-in features like AutoML and a scheduler for recurring tasks. Retrain automations and integrate custom code to keep processes aligned and relevant.
• Deploy scalable working models securely and reliably in one-click. Tailor deployments using REST APIs or as a component to generate batch predictions. Create dashboards to share with stakeholders, and swiftly update your model without worrying about service interruptions or breakages.
• Monitor infrastructure and model behavior to understand resource utilization and how data changes over time—without requiring more of IT. Put an end to endless maintenance meetings with reliable, real-time monitoring applications around drift, data in-and-out, and prediction distribution. If an issue arises, Prevision.io provides detailed alerts and analysis to understand the root of a problem.

Any business can benefit

Regardless of industry or department, we’ve seen Prevision.io help businesses solve some of their biggest challenges. Utilities companies are relying on better forecasts of the energy consumption (gas or electricity).. Transportation companies have deployed machine learning models that can inform logistical operations based on fluctuating supply and demand. Doing more with data not only improves what a business can offer their customers but can also yield significant savings. Here are a few real-life examples:

La Poste: delivery data saves the day
Global delivery company La Poste was having trouble meeting customers’ demand for speed and visibility, and inaccurate estimated arrival times for packages was costing them money. The team wanted to put its tracking and tracing data to work, and turned to Prevision.io to select technical metrics, set up personalized machine learning models for delivery rounds of all personnel, and speed up the iteration and training process. After deploying its model in four weeks, La Poste achieved an 89% accuracy rate for delivery times and saw a 10x improvement in IT infrastructure and operational costs. And of course, happier customers who keep coming back.

BPCE: machine learning helps us help our clients
An arm of BPCE Group, the second largest banking group in France, was feeling the effects of the pandemic’s impact on customers. It needed a more efficient way of determining who would need what type of help—and when–to reduce the number of customers entering the collections process. Using its wealth of data to create and deploy a machine learning model in Prevision.io, the firm was able to rapidly identify the most at-risk customers and better understand the root causes behind potential debt default—some of which are easily fixable. As a result, the firm has seen a fifteen-fold increase in the sums they have been able to recover, and decreased the number of collection cases by 50%.

Pharmaceutical company: marketing medicine with MLOps
The marketing department at a healthcare company serving pharmacies was able to reduce customer churn and improve growth by using Prevision.io to compute market segmentation based on anticipated customer revenue. This helped the company determine the best way to engage with each pharmacy—and when. Being able to make strategic decisions based on automated predictions and more targeted data saved the company $1.3M Euros in two fiscal quarters.

See how AI project management and MLOps made easy can transform your business. Access Prevision.io on Google Cloud Marketplace and take advantage of the 14-day free trial.