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.

When customers migrate to Google Cloud Platform (GCP), their first step is often to adopt Compute Engine, which makes it easy to procure and set up virtual machines (VMs) in the cloud that provide large amounts of computing power. Launched in 2012, Compute Engine offers multiple machine types, many innovative features, and is available in 20 regions and 61 zones! 

Compute Engine’s predefined and custom machine types make it easy to choose VMs closest to your on-premises infrastructure, accelerating the workload migration process cost effectively. Cloud allows you the pricing advantage of ‘pay as you go’ and also provides significant savings as you use more compute with Sustained Use Discounts. 

As Technical Account Managers, we work with large enterprise customers to analyze their monthly spend and recommend optimization opportunities. In this blog, we will share the top recommendations that we’ve developed based on our collective experience working with GCP customers. 

Getting ready to save

Before you get started, be sure to familiarize yourself with the VM instance pricing page—required reading for anyone who needs to understand the Compute Engine billing model and resource-based pricing. In addition to those topics, you’ll also find information about the various Compute Engine machine types, committed use discounts and how to view your usage, among other things. 

Another important step to gain visibility into your Compute Engine cost is using Billing reports in the Google Cloud Console and customizing your views based on filtering and grouping by projects, labels and more. From there you can export Compute Engine usage details to BigQuery for more granular analysis. This allows you to query the datastore to understand your project’s vCPU usage trends and how many vCPUs can be reclaimed. If you have defined thresholds for the number of cores per project, usage trends can help you spot anomalies and take proactive actions. These actions could be rightsizing the VMs or reclaiming idle VMs.

Now, with these things under your belt, let’s go over the five ways you can optimize your Compute Engine resources that we believe will give you the most immediate benefit. 

1. Apply Compute Engine rightsizing recommendations

Compute Engine’s rightsizing recommendations feature provides machine type recommendations that are generated automatically based on system metrics gathered by Stackdriver Monitoring over the past eight days. Use these recommendations to resize your instance’s machine type to more efficiently use the instance’s resources. It also recommends custom machine types when appropropriate. Compute Engine makes viewing, resizing and other actions easier right from the Cloud Console as shown below. 

Recently, we expanded Compute Engine rightsizing capabilities from just individual instances to managed instance groups as well. Check out the documentation for more details.

For more precise recommendations, you can install the Stackdriver Monitoring agent which collects additional disk, CPU, network, and process metrics from your VM instances to better estimate your resource requirements. You can also leverage the Recommender API for managing recommendations at scale.

2. Purchase Commitments

Our customers have diverse workloads running on Google Cloud with differing availability requirements. Many customers follow a 70/30 rule when it comes to managing their VM fleet—they have constant year-round usage of ~70%, and a seasonal burst of ~30% during holidays or special events. 

If this sounds like you, you are probably provisioning resources for peak capacity. However, after migrating to Google Cloud, you can baseline your usage and take advantage of deeper discounts for Compute workloads. Committed Use Discounts are ideal if you have a predictable steady-state workload as you can purchase a one or three year commitment in exchange for a substantial discount on your VM usage.

We recently released a Committed Use Discount analysis report in the Cloud Console that helps you understand and analyze the effectiveness of the commitments you’ve purchased. In addition to this, large enterprise customers can work with their Technical Account Managers who can help manage their commitment purchases and work proactively with them to increase Committed Use Discount coverage and utilization to maximize their savings.

3. Automate cost optimizations

The best way to make sure that your team is always following cost-optimization best practices is to automate them, reducing manual intervention.

Automation is greatly simplified using a label—a key-value pair applied to various Google Cloud services. For example, you could label instances that only developers use during business hours with “env: development.” You could then use Cloud Scheduler to schedule a serverless Cloud Function to shut them down over the weekend or after business hours and then restart them when needed. Here is an architecture diagram and code samples that you can use to do this yourself. 

Using Cloud Functions to automate the cleanup of other Compute Engine resources can also save you a lot of time and money. For example, customers often forget about unattached (orphaned) persistent disk, or unused IP addresses. These accrue costs, even if they are not attached to a virtual machine instance. VMs with the “deletion rule” option set to “keep disk” retain persistent disks even after the VM is deleted. That’s great if you need to save the data on that disk for a later time, but those orphaned persistent disks can add up quickly and are often forgotten! There is a Google Cloud Solutions article that describes the architecture and sample code for using Cloud Functions, Cloud Scheduler, and Stackdriver to automatically look for these orphaned disks, take a snapshot of them, and remove them. This solution can be used as a blueprint for other cost automations such as cleaning up unused IP addresses, or stopping idle VMs. 

4. Use preemptible VMs

If you have workloads that are fault tolerant, like HPC, big data, media transcoding, CI/CD pipelines or stateless web applications, using preemptible VMs to batch-process them can provide massive cost savings. In fact, customer Descartes Labs reduced their analysis costs by more than 70% by using preemptible VMs to process satellite imagery and help businesses and governments predict global food supplies.

Preemptible VMs are short lived— they can only run a maximum of 24 hours, and they may be shut down before the 24 hour mark as well. A 30-second preemption notice is sent to the instance when a VM needs to be reclaimed, and you can use a shutdown script to clean up in that 30-second period. Be sure to fully review the full list of stipulations when considering preemptible VMs for your workload. All machine types are available as preemptible VMs, and you can launch one simply by adding “-preemptible” to the gcloud command line or selecting the option from the Cloud Console. 

Using preemptible VMs in your architecture is a great way to scale compute at a discounted rate, but you need to be sure that the workload can handle the potential interruptions if the VM needs to be reclaimed. One way to handle this is to ensure your application is checkpointing as it processes data, i.e., that it’s writing to storage outside the VM itself, like Google Cloud Storage or a database. As an example, we have sample code for using a shutdown script to write a checkpoint file into a Cloud Storage bucket. For web applications behind a load balancer, consider using the 30-second preemption notice to drain connections to that VM so the traffic can be shifted to another VM. Some customers also choose to automate the shutdown of preemptible VMs on a rolling basis before the 24-hour period is over, to avoid having multiple VMs shut down at the same time if they were launched together. 

5. Try autoscaling 

Another great way to save on costs is to run only as much capacity as you need, when you need it. As we mentioned earlier, typically around 70% of capacity is needed for steady-state usage, but when you need extra capacity, it’s critical to have it available. In an on-prem environment, you need to purchase that extra capacity ahead of time. In the cloud, you can leverage autoscaling to automatically flex to increased capacity only when you need it. 

Compute Engine managed instance groups are what give you this autoscaling capability in Google Cloud. You can scale up gracefully to handle an increase in traffic, and then automatically scale down again when the need for instances is lowered (downscaling). You can scale based on CPU utilization, HTTP load balancing capacity, or Stackdriver Monitoring metrics. This gives you the flexibility to scale based on what matters most to your application. 

High costs do not compute

As we’ve shown above, there are many ways to optimize your Compute Engine costs. Monitoring your environment and understanding your usage patterns is key to understanding the best options to start with, taking the time to model your baseline costs up front. Then, there are a wide variety of strategies to implement depending on your workload and current operating model. 

For more on cost management, check out our cost management video playlist. And for more tips and tricks on saving money on other GCP services, check out our blog posts on Cloud Storage, Networking and BigQuery cost optimization strategies. We have additional blog posts coming soon, so stay tuned!

Product teams often serve as the nexus between all organizational units within a company, ensuring that a specific line of business follows a clear roadmap and that cross-functional alignment drives success. At their most empowered, product owners are like miniature CEOs for each offering within a company.

Unfortunately, in today’s complicated IT landscape, not everything that should be thought of as a product receives the designation. In my time as a digital consultant, I’ve seen one particular technology too often dismissed as back-office minutiae — application programming interfaces, or APIs.

APIs can be the glue that binds a company’s resources together and the mechanisms through which an enterprise’s data, functionality, and other digital assets are accessed and distributed. They enable software systems to communicate and let developers leverage software for new applications and digital experiences, abstracting underlying complexity into a consistent interface that allows data and functionality to be combined and reused with unprecedented flexibility.

Unfortunately, many business leaders see APIs as middleware and thus as undeserving of the resources of a full-fledged digital product. Their organizations create APIs as one-off projects, with engineers moving on to other projects when they are done and no one looking after an API’s maintenance or whether it offers utility beyond the bespoke use case for which it was created.

This is a problem because APIs can unlock digital assets that otherwise would be captive within enterprise silos. An API that exposes a customer database can likely be used and reused for numerous purposes, for example, and treating developers as customers of this API can help increase adoption of and the amount of innovation around it. But such a powerful outcome is unlikely if an API has been created only as a system integration detail rather than as a product with an owner who oversees a full lifecycle, from ideation to adoption to iteration. Put bluntly, a project-oriented attitude toward APIs misses the boat.

Pitney Bowes, a company I’ve worked with at Google Cloud, offers a high-profile example in which a notable enterprise has treated APIs as products and given its API program the same treatment it would give to any go-to-market offering. After selling traditional mailing and shipping solutions for nearly 100 years, the company transformed itself into a provider of digital logistics and e-commerce solutions by building out 160 public APIs and 400 internal APIs. This enabled its developers and partners to easily consume the company’s location intelligence and shipping services in new, agile ways. It also accelerated the company’s internal project timelines, shortening the average project lifecycle from over a year to just a few months. What’s more, APIs not only introduced efficiencies and reduced costs, but also opened new revenue streams. Because developers can more easily leverage individual Pitney Bowes business capabilities for their applications, the company’s services are manifesting in new ways — such as when Pitney Bowes technology is harnessed to let customers print USPS shipping labels from within the context of an online marketplace.

Manage APIs as products

Traditionally, but wrongly, APIs have been considered pieces of other products. They’ve been built by the IT team at the request of product owners every time a project came up that required one. When the funding spigot is flowing, APIs generally receive support. But once the project runs its course, the APIs are neglected and quickly grow outdated.

Without consistent resources, APIs can’t be adapted to fluctuations in the enterprise and market dynamics. Worst of all, project-oriented APIs often end up siloed within the business unit that gave birth to them, which defies the benefit of modern APIs: to not only allow enterprise assets to be reused by other business units and partners but also accelerate the business’s innovation by increasing the number of developers interacting with its capabilities.

In contrast, if an API is treated as a product in its own right with its own owner lobbying for consistent funding, the API can be iterated according to shifts in internal and market trends. Ideally, the product owner should continually monitor an API’s engagement and collect and synthesize analytics to improve quality of service. Product-minded companies frequently launch APIs as minimum viable products in order to get ideas to market faster, then use API consumption analytics to understand developer engagement and refine APIs to be even more attractive, powerful, and intuitive.

With the help of management tools that facilitate dynamic changes, an API product owner can also help ensure that developers benefit from the latest and greatest build with minimal interruption.

Virtually all traditional product ownership responsibilities can be easily applied to an API. An API product owner is responsible for galvanizing the entire company around their API’s value. Their team can answer any questions users may have about the API’s value proposition or how to use it. It may sound trivial but having dedicated experts available to assist with any question that may arise can be a boon to an API’s velocity.

Additionally, if a dedicated product owner is assigned to pilot a company’s aggregate API program, they can use comprehensive monitoring and analysis of all API traffic to derive insight into which APIs drive value and which don’t. This can save considerable time and money from being wasted on the wrong bets. The product owner can also create API bundles segmented according to each developer community the program wants to serve. Here, the goal is not just to expose enterprise assets, but also to encourage repeat consumption. When it comes to third-party engagement, these bundles should seek to improve partner stickiness to cultivate and maintain new revenue channels. Just as any product is only useful if it has users adopting it and sticking with it, APIs are typically valuable when they attract communities of repeat users.

Tools and processes that turn APIs into strategic assets

To drive an API as a strategic asset, an API product manager should support the API with management tools and processes.

First off, it’s necessary to establish comprehensive security layers for an API so cross-functional internal groups and third-party partners are comfortable using it. Mechanisms that apply protection with introducing user friction — such as OAuth, API key verification, XML/JSON threat protection — are strongly encouraged. It’s also often prudent to integrate machine learning functionality that can detect bot attacks and other adversaries before they breach an API and affect either its developer users or the end users consuming those developers’ apps.

Enterprise software products are typically afforded visualization tools, dashboards, reports, and self-service functionality to drive adoption — and APIs should be no different. Comprehensive portals that highlight API features (including knowledge centers, interactive documentation, blogs, and forums) should be crafted to promote developer consumption. Anything that is likely to magnetize collaborators and expedite registration should be provided for an API. A primary goal of API-first development is to increase velocity, so offering developers a self-service experience while maintaining visibility and control over API usage is crucial.

Manage with an “outside-in” perspective

Perhaps the most important role an API product owner must fulfill is maintaining a staunch outside-in approach.

In an inside-out perspective, enterprise leaders look internally for gaps and fill them with existing IT capabilities. Internal intuition untested by the market can define planning, and the resources that IT already has — rather than those that customers demand — typically restrict options. In contrast, an outside-in approach requires the company to listen to what the market is asking for and to respond appropriately. Outside-in thinkers start by asking what the customer needs, then build strategies from there. Because customer preferences are moving targets, an outside-in perspective is constantly evolving and seeking new data. In the case of API programs, product managers should find ways to align API user data with business KPIs, which can be essential to organizing stakeholders and achieving buy-in for the program throughout the organization.

Because both consumers and developers will abandon products that do not meet their needs, an outside-in perspective is increasingly non-negotiable. The product owner needs to analyze rapidly-changing market trends, understand the competitive landscape, keep a pulse on customer and stakeholder needs, and facilitate a backlog of changes that can help the company satisfy the market. Essentially, API product owners are the end-user advocate — and that’s for both for developers using the APIs and the people touching the applications powered by those APIs.

None of this means product owners need to be entirely risk averse, however. They should have the freedom to experiment with hypotheses and to make adjustments — but to make experimentation viable over the long run, they also need to have the foresight to kill a project immediately if the early end-user response is poor. If the product owner is not learning and iterating, they probably are not pushing the API program forward.

Calling all product professionals

If an enterprise plans to pursue an API-first mandate, it’s crucial that the API program is a well-funded, autonomous business unit. Without a visionary owner, APIs will not be able to usher in the transformation experienced by companies such as Pitney Bowes. The API-first philosophy requires that APIs are built prior to applications and that the API provide a value proposition of its own — which means that if an enterprise wants to accelerate innovation and take advantage of APIs, its first step should be to hire API product managers.

Tradier uses the Apigee API management platform from Google to abstract the legacy complexities of capital markets so that developers can build FinTech applications in an agile, nimble, and quick fashion at minimal cost. The company embodies the evolution of what cloud technology can enable in the form of an API-first business delivered as a service.

The rise of API-powered FinTech

Historically, companies that wanted to build systems, applications, or services to interact with the stock market would have to build an entire brokerage operation from scratch. This would include data infrastructure, compliance infrastructure, and storage capabilities. It could take years and massive capital expense to accomplish everything that was required to be ready to serve customers.

Tradier provides this infrastructure as an API-based service so that the same companies can launch investor applications in as little as a few weeks. This democratized access means that FinTech innovation can come from anywhere, giving the same opportunities to create new products to everyone, from enterprise customers to startups.

“Financial markets are becoming fundamentally decentralized and unbundled,” says Dan Raju, co-founder, CEO, and chairman at Tradier. “The services that large legacy banks and brokerage firms used to offer are being supplanted by Tradier’s microservices and APIs, which power innovation.”

More than 200 companies use Tradier to develop and launch new products, or to add new features and functions that they traditionally would not have offered in existing products. With a large and diverse user base, Tradier faced the challenge of managing its partners in way that helps ensure it can grant credentials, track, monitor, and report in an efficient and equitable manner.

At the same time, the company recognized the inherent value of its partners for their power to leverage Tradier APIs to innovate. Tradier’s fundamental market disruption is the partner ecosystem, where the company is engaged along with its partners to deliver value to the entire ecosystem in the form of new products and services.

Embracing an API-powered ecosystem

Tradier has moved beyond providing great APIs toward engaging its ecosystem. If a customer wants a specific dataset, the company doesn’t automatically build a new product. Instead, Tradier looks to the ecosystem to build the product. With this approach, Tradier has taken its capabilities and multiplied them by hundreds.

“The fundamental difference between thinking about an API ecosystem versus an API product is the difference between being a participant who’s enabling innovation and not just a company delivering a set of technical capabilities,” Raju says.

Tradier takes an outside-in approach toward engaging its API ecosystem. Constantly listening to participants and helping to enable and empower them to create value is fundamental to the company’s business model. Rather than simply focusing on building new capabilities on its own, Tradier listens to what functionalities customers need and facilitates development. In many cases, the ecosystem generates the requested product organically rather than Tradier needing to do it.

“I love APIs because they allow you to empower others to create value. The concept of empowering others to create value along with you is what is the most satisfying, and the most fascinating, thing about APIs,” says Raju.

Delivering value at scale

Tradier handles between 500 million to 1 billion API calls and a billion dollars in transactions a month, and all of them run through the Apigee API management platform. Apigee’s last mile forms the single layer that manages Tradier’s infrastructure, including security, analytics, developer interactions, and execution. The company also uses Apigee to comply with an array of regulatory reporting, mandated by Tradier’s status as a FINRA (Financial Industry Regulatory Authority)-regulated entity.

“Apigee is integral to the Tradier offering. They have been great partners and have always collaborated and enabled us to innovate at a pace that helped Tradier attract developers and innovative companies. We see tremendous potential in the synergy of Apigee and Google as it brings to the market a vast extended capability set based on the Google Cloud Platform.”

Tradier is an API-centric ecosystem that delivers value to an entire set of players where the nucleus is the Apigee API management suite, which helps deliver, innovate, publish, manage, monitor, and secure the ecosystem on a day-to-day basis. Simplicity combined with product evangelism is the key to success in the API space, Raju says.

Tradier’s capabilities to innovate, iterate, and travel the journey with its customers, partners, and developers has yielded many rewards. The company’s long history of working with Apigee has enabled it to assemble a set of people and resources for creating engagement, as well as to create a winning set of APIs for delivering FinTech capabilities.

Considering the transformative future

Looking toward a future in which the financial services industry will experience ongoing disruption, Raju predicts that Tradier will continue to leverage APIs to lead the way.

“I think traditional banks are under attack. They are being replaced by a set of nimble, agile players that are offering a lot of new functionality to customers. This is forcing banks to think about how they can digitize their products through APIs so that they can provide the same functionalities as newer players.”

With companies like Tradier and others offering functionality that used to be in-house and exposing it outside, traditional brokerage firms are also being forced to rethink their model. Raju believes that this line of thinking also extends to the Blockchain.

“Exposing Blockchain-like capabilities through APIs is going to be a disruptive influence, and it will be critical for companies to think about how APIs, and more importantly Blockchain, can create value for us.”

Many of today’s games are rich, immersive worlds that engage the audience in ways that make a gamer a part of a continuing storyline. To create these persistent experiences, numerous storage technologies are required to ensure game data can scale to the standards of gamers’ demands. Not only do game developers need to store different types of data—such as saves, inventory, patches, replays, and more—but they also must keep the storage system high-performing, available, scalable, and cost-effective.

Enter Open Saves, a brand-new, purpose-built single interface for multiple storage back ends that’s powered by Google Cloud and developed in partnership with 2K. Now, development teams can store game data without having to make the technical decisions on which storage solution to use, whether that’s Cloud Storage, Memorystore, or Firestore. 

“Open Saves demonstrates our commitment to partnering with top developers on gaming solutions that require a combination of deep industry expertise and Google scale,” said Joe Garfola, Vice President of IT and Security at 2K. “We look forward to continued collaboration with Google Cloud.”

Game development teams can save game data against Open Saves without having to worry about the optimal back-end storage solution, while operations teams can focus on needed scalability and storage options. Here’s how it looks in practice:

With Open Saves, game developers can run a cloud-native game storage system that is:

• Simple: Open Saves provides a unified, well-defined gRPC endpoint for all operations for metadata, structured, and unstructured objects.
• Fast: With a built-in caching system, Open Saves optimizes data placements based on access frequency and data size, all to achieve both low latency for smaller binary objects and high throughput for big objects.
• Scalable: The Open Saves API server can run on either Google Kubernetes Engine or Cloud Run. Both platforms can scale out to handle hundreds of thousands of requests per second. Open Saves also stores data in Firestore and Cloud Storage, and can handle hundreds of gigabytes of data and up to millions of requests per second.

Open Saves is designed with extensibility in mind, and can be integrated into any game—whether mobile or console, multiplayer or single player—running on any infrastructure, from on-prem to cloud or a hybrid. The server is written in Go, but you can use many programming languages and connect from client or server since the API is defined in gRPC.

Writing to and reading from Open Saves is as simple as the following code:

  // To write
	record := &pb.Record{
		Key:      uuid.New().String(),
		Tags:     []string{"tag1", "tag2"},
		OwnerId:  "owner",
	}
	createReq := &pb.CreateRecordRequest{
		StoreKey: storeKey,
		Record:   record,
	}
	_, err := client.CreateRecord(ctx, createReq)
	if err != nil {
		t.Fatalf("CreateRecord failed: %v", err)
	}
	// To read
	getReq := &pb.GetRecordRequest{StoreKey: storeKey, Key: recordKey}
	response, err := client.GetRecord(ctx, getReq)
	if err != nil {
		t.Errorf("GetRecord failed: %v", err)
	}

We are actively developing Open Saves in partnership with 2K Games, and would love for you to come join us on GitHub. There are a few ways to get involved:

• Install and deploy your Open Saves service
• Check out the API reference
• Read the development guide and start contributing
• Join the open-saves-discuss mailing list