Insurer Uses Google Cloud AI to Battle Slow Growth: It Improves Sales by 5% in 8 Weeks
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For a business to succeed in the long term, it needs to learn not just to adapt to inevitable change, but to harness it. South Africa-based PPS has been an insurance company since 1941 and today is the biggest mutual insurance provider in the country.
As a mutual company, PPS is owned by more than 200,000 members, making them shareholders. In recent years, PPS and other companies like it have been affected by a number of external factors.
“For one thing, technology platforms have brought in a new gig economy that has all kinds of implications for insurance,” says Avsharn Bachoo, CTO at PPS. “What we’ve been seeing is basically a disruption of the South African insurance industry. We chose to see that as an opportunity.”
“Our servers were at the end of their life cycle and we had to decide whether to refresh them or switch completely. To embrace the world of AI and machine learning effectively, we knew we needed a cloud-based infrastructure. We’ve found the answer in Google Cloud Platform.”
—Avsharn Bachoo, CTO, PPS
In early 2018, faced with an uncertain economic environment that was squeezing growth and profitability, PPS decided to transform itself from a traditional broker-based business into a digital insurance provider. A key pillar of this new strategy was to overhaul the company’s technology infrastructure. To turn the strategy into reality, Avsharn and his team chose Google Cloud Platform (GCP).
“Our servers were at the end of their life cycle and we had to decide whether to refresh them or switch completely,” says Avsharn. “To embrace the world of AI and machine learning (ML) effectively, we knew we needed a cloud-based infrastructure. We’ve found the answer in Google Cloud Platform.”
Power, speed, flexibility with Google Cloud Platform
Previously, PPS maintained an on-premises IT infrastructure, which worked for its traditional business but was unsuited for its new way of working. In early 2018, the company started working on new products for its members but this required large amounts of compute power that proved prohibitively expensive with on-premises servers. Even existing products were starting to require more than the infrastructure could deliver. Aging equipment meant that it’s testing and quality assurance environments bore little resemblance to the actual production environment.
“We had no pre-production environments at all,” says Avsharn, resulting in more work for developers after products had been released. Meanwhile, the capital required to buy and configure more servers for new projects meant fewer resources available for innovation, and left the company less able to react to changes in the market. PPS knew it had to find a cloud-based alternative.
Shortly after devising a new digital strategy, PPS engineers attended a training session on cloud infrastructure given by leading South African Google Cloud Partner Siatik. Impressed with the presentation, PPS engaged Siatik to help run a proof of concept for a cloud-based infrastructure, running on GCP. With on-site engineers and constant communication, Siatik formed a very close working relationship with PPS. “The team at Siatik was exemplary,” recalls Avsharn. “They were well-organized, with cutting-edge technical acumen and very creative solutions to our problems. They were real game-changers.”
“We wanted the platform to retrain its models in response to new data and improve its recommendations with more information. Normally this would be a manual process but Google Cloud ML Engine lets the models do this automatically.”
—Kimoon Kim, Lead Solution Architect and Data Engineer, Siatik
The proof of concept was successful, with GCP outperforming the existing infrastructure in terms of how it handled compute demands, databases, and storage.
“It’s the speed of GCP that really impresses us,” says Avsharn. PPS saw that GCP wasn’t just an opportunity to migrate its existing infrastructure to the cloud. With Siatik’s help, it redesigned its monolithic core architecture to one based around microservices using Google Kubernetes Engine (GKE). For data processing and storage, Cloud Dataflow and Cloud Datastore proved invaluable, while Stackdriver helped the IT team stay on top of logging and monitoring the system.
“Google Cloud makes migrations very easy,” says Brett St. Clair, CEO at Siatik. “It takes care of all the hard work with configurations and replications, so when we switch the machines on, everything is ready and working.”
The ease with which PPS migrated to GCP means that it can now tackle strategic goals much more quickly than before. The most ambitious of these is an AI-powered product recommendation platform. Information is collected from customers who opt in at a defined point in their journey, this database is queried using BigQuery, and the information is fed into the platform. The AI model then calculates the most appropriate products for each member, according to their personal history.
“Most of the product recommendation engines out there are based on clustering, where you’re offered products based on your peer groups,” explains Avsharn. “For the first time, we can make recommendations to members based on their individual preferences and historical behavior. That’s really powerful for us.”
Siatik helped PPS use TensorFlow and Cloud Machine Learning Engine to build the AI platform. For the engineers, these easy-to-use tools helped speed up the process considerably, allowing them to host the models locally without any fuss. Previously, it took one to three months to manually build the model and match an offer to a customer. With the AI platform, a match takes just a few minutes. Cloud ML Engine, in particular, helped the platform adapt to new information on the fly and easily make adjustments to its hyperparameters, that is, preset variables which define the model-training process.
“We wanted the platform to retrain its models in response to new data and improve its recommendations with more information,” says Kimoon Kim, Lead Solution Architect and Data Engineer at Siatik. “Normally this would be a manual process but Google Cloud ML Engine lets the models do this automatically.”
“Google Cloud helped us cancel out a lot of the noise around machine learning and AI. We don’t have to build new complicated algorithms or hire huge teams of data scientists to benefit. We just bring our data and use the right tools to focus on what’s really important.”
—Avsharn Bachoo, CTO, PPS
Harnessing artificial intelligence for real-world results
PPS deployed its new AI recommendation platform in December, 2018. Just a couple of months later, its impact was clear. “In around eight weeks, we saw a 5 percent growth in sales,” says Avsharn. “It’s been a direct result of building our recommendation platform with Google Cloud. We can offer the right products to the right members.”
For developers and engineers at PPS, working with Google Cloud gives them access to high performance technology and automation options with GKE. As a result, the infrastructure runs 70 percent faster than before with fewer cores and less memory. Developers can also work in mature testing environments, and for the first time, are able to build pre-production environments, leading to better quality products. More strategically, moving to a serverless, cloud-based infrastructure has helped PPS take control of its budget, moving away from intermittent, large capital spends to more manageable, project-to-project flows of operational expenditure. The company expects to see savings of around 50 percent, or $695,000.
“We have a lot more flexibility with our resources thanks to Google Cloud,” says Avsharn. “When we have a new idea, we don’t have to outlay new capital such as servers before we can even start working on it. We just spin up instances when we want and spin them back down when we’re done.”
With the AI platform deployed and working well, PPS is already looking at ways to improve it, including real-time updates and further automation. Soon, the company will integrate the platform with more sales campaigns for more effective targeting to boost sales even further. Meanwhile, it’s also experimenting with machine learning to spot patterns in data at scale for fraud analytics and risk assessment.
For PPS, working with Google Cloud has helped it transform quickly and effectively from disrupted to disruptor. The company is now looking to gain the same transformative effects by implementing G Suite for increased productivity and collaboration.
“Google Cloud helped us cancel out a lot of the noise around machine learning and AI,” says Avsharn. “We don’t have to build new complicated algorithms or hire huge teams of data scientists to benefit. We just bring our data and use the right tools to focus on what’s really important.”
Google is a Leader in the 2019 Gartner Magic Quadrant for Data Management Solutions for Analytics
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As organizations continue to produce vast quantities of data, they increasingly need platforms that allow them to analyze, store, and extract meaningful insights from that data. And research from analyst firms like Gartner offer an important way for organizations to evaluate and compare cloud data warehouse providers.
Earlier this year, Gartner named Google a Leader in the 2019 Gartner Magic Quadrant for Data Management Solutions for Analytics (DMSA) (report available here). This evaluation covers Google Cloud’s core data analytics offerings, including BigQuery, a serverless, managed data warehouse, Cloud Dataproc, a managed Spark and Hadoop service, and Cloud Dataflow, which enables you to stream and batch-process your data. Here are a few takeaways:
Simplicity and speed
BigQuery’s performance permits complex queries on large-scale data sets to return in seconds, and a substantial number of BigQuery customers maintain data warehouses that store more than 50 terabytes (and a few customers now use more than 100 petabytes). More than half of these customers are loading data either continuously or many times per day. These customers value the ability to extract, transform, load, and analyze their data on a serverless platform, all without maintaining any underlying infrastructure.
A versatile serverless data warehouse
One of BigQuery’s major advantages is its ability to allow customers to address a wide variety of use cases—from a traditional data warehouse to data science. Over the past year, we’ve worked hard to introduce new features in BigQuery like data types for financial and monetary uses, BigQuery GIS for geospatial data, and machine learning capabilities through BigQuery ML. BigQuery’s continuous ingest capabilities make it suitable for an operational—or a real-time—serverless data warehouse.
An expanding ecosystem
With overall increased market adoption, our analytics offerings continue to benefit from a fast-growing partner ecosystem of service providers, and business intelligence (BI) and data integration vendors. In particular, in 2018 we expanded our partnerships with established industry providers, including Confluent, Dell Boomi, Informatica, Looker, Reltio, Tableau, and ThoughtSpot.
More and more organizations are finding value in Google Cloud’s serverless data warehouse and analytics offerings. If you’d like to learn more, you can download a complimentary copy of the Gartner Magic Quadrant for Data Management Solutions for Analytics on the Google site (requires an email address).
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.
CCAI Platform goes GA: Deliver World-class CX and Accelerating Time-to-value with AI
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Customers reach out to contact centers for help in moments of urgent need, but due to increasing demands, new channels, peak times, and operational pressures, contact centers often struggle to provide timely help. To bridge this gap, enterprises are increasingly investing in AI-driven solutions that balance addressing customer expectations with operational efficiency.
But building and generating value from such solutions can be complicated and challenging. Google Cloud built Contact Center AI (CCAI) to streamline and shorten this time to value, and CCAI Platform, our newest addition, takes a crucial step in this effort by introducing end-to-end call center capabilities. After debuting these new capabilities in March, we are excited to announce that CCAI Platform is now generally available across the US, Canada, UK, Germany, France, Italy and Spain—with more markets soon to come.
Delivering world-class customer experiences and accelerating time-to-value with CCAI
CCAI encompasses a comprehensive set of offerings to address the top pain points of the three main user groups in the contact center: contact center owners, their agents, and the customers they serve.
Dialogflow lets the contact center manager scale their operations while balancing cost and customer satisfaction, including reducing painful, long waiting times endured by end users. Using Dialogflow, contact center managers can build complex chat and voice virtual agents—a proven, cost-effective way to scale contact centers while continuing to provide great customer experiences. Available 24/7, without any waiting queue, these virtual agents can converse naturally with customers, identify their issues, and address them effectively.
Agent Assist reduces overall handling time and coaches human agents to become more effective and helpful. The service uses AI to “listen” to the voice and chat conversations between the human representative and the customer, then provides real-time guidance and recommendations to the agent, based on historical conversations, knowledge bases, and best practices of experienced agents. It also automates post-call actions such as transcription and call summarization, saving significant time and overhead at the end of every call.
CCAI Insights stores and analyzes all the customer conversations in the contact center, whether with human or virtual agents, to provide leaders with real-time, actionable data points on customer queries, agent performance, sentiment trends, and opportunities for automation.
At the heart of these technologies is our conversational AI brain. It uses Google Research’s technology to talk, understand, and interact, enabling and orchestrating high-quality conversational experiences at scale.
CCAI Platform: a modern CCaaS and the shortest path to CCAI value
While the value of the CCAI offerings is clear to our customers, we also hear from them that integrating these solutions with legacy infrastructure takes too long.
To minimize these integration difficulties, accelerate time-to-value using the CCAI offerings, and help businesses provide outstanding customer experiences, we’re pleased to announce the general availability of CCAI Platform, the Contact Center as a Service (CCaaS) solution from Google Cloud built in partnership with UJET.
CCAI Platform is a modern, turnkey Contact Center as a service solution, designed with user-first, AI-first, and mobile-first principles. It offers:
- Turnkey core Contact Center capabilities out-of-the-box, for faster time to production, lower implementation overhead, and custom development needed
- AI-powered experiences, from routing to better handling customer interactions
- Deep integration with CCAI’s offerings, to provide a unified end-to-end experience for contact center transformation
- Mobile-first design that enables interactions in line with the way people expect to communicate across channels
- CRM-centered design with automated updates, so agents can focus on the customer
- Deployment flexibility, with customer data residing in their CRM and the flexibility to bring their own telephony carrier to minimize cost
All of this is available without the typical need to integrate complex technologies from multiple providers.
“With Google Cloud and CCAI Platform, we will quickly move our contact center to the cloud, supporting both our customers and agents with industry-leading CX innovations, all while streamlining operations through more efficient customer care operations,” said Dean Kontul, Division CIO of KeyBank.
For customers looking to change platforms for a cloud-native CCaaS with deep Google AI integrations, CCAI Platform offers end-to-end capabilities that accelerate call center transformations. We also remain strongly committed to customer choice, and customers will continue to have the option to integrate our latest and greatest CCAI offerings through our existing OEM partners.
The Contact Center conversation is just beginning
This launch is part of a broader effort to deliver more value, faster, to more CCAI customers. As companies replace interactive voice response (IVR) with intelligent virtual agents (IVA) and begin to collect and analyze data, use cases are likely to grow more sophisticated—which is one reason Google Cloud is continuing to invest in technologies to make our CCAI offerings even more useful, as well as best practices like the following:
- CCAI Agent Assist and Insights are a great first step in AI transformation. They let contact center owners enable call transcription and use Topic Modeling to identify conversation themes that demand attention. Human agents can automatically generate high-quality conversation summaries to reduce call wrap-up time, and the associated costs, while improving business insights. We are working to make these features available both in CCAI Platform and with our partner ISVs.
- Chat and call steering are the first step for IVA automation. Another area of broad impact is conversational chat or call steering, in which friction is reduced by routing customers to the correct virtual or live agent experience. Many call centers rely on IVR systems in which customers have to use a keypad to select an option. Enterprise leaders tell us that attrition is very high throughout this process: some customers angrily hang up without resolution and, just as bad, many simply pound a single key in hopes of reaching a human agent, leading to the customer reaching the wrong person because their issue was never correctly identified or routed. Using Dialogflow’s natural language understanding (NLU) capabilities can sweep away such problems, with the customer more likely to not only reach the appropriate resources, but also share conversational data from which insights can be gleaned. It’s an approach that can pay dividends right away, and a quick first step to IVA automation.
In coming months, we will continue to work on these and other capabilities that are targeted to deliver higher and quicker value to our customers. We plan to release pre-built components to help companies tackle call center use cases in specific industries, for example. We’ll also continue to partner with companies that share our vision of transforming customer experiences with AI, such as TTEC, a provider of customer experience technology and software.
“TTEC Digital and Google Cloud have a shared vision for transforming global CX delivery through artificial intelligence, digital innovation, and operational excellence,” said Sam Thepvongs, VP of TTEC Digital. “With CCAI Platform, we can offer our largest enterprise customers a strategic blueprint for moving to the cloud while adopting a leading, AI-powered contact center platform. We couldn’t be more excited about this evolution of Google Cloud’s groundbreaking CCAI portfolio, and the opportunity to help our customers digitally transform their CX through this partnership.”
To get started with CCAI Platform, visit our solutions page or checkout our new omni channel demo video—and don’t forget to join us at Google Cloud Next ’22, where I’ll be sharing exciting new updates for CCAI in my session, “Delight customers in every interaction with Contact Center AI.”
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Technical deep dive on Looker: The enterprise BI solution for Google Cloud
Thousands of users accessing petabytes of data on a daily basis: This is a challenging proposition for enterprises, without a doubt.
But Looker makes it possible. Beyond just accessing data though, Looker’s platform transforms your company’s relationship with data.
Go under the hood of Looker, with Olivia Morgan, Enterprise CE, Looker, to see how LookML empowers developers to take advantage of powerful data warehouses like BigQuery and ultimately enhance the workflows of end users.
She also takes a deep dive into LookML’s ability to use Google Cloud Functions and Search API to enhance dashboards, leverage BQML within Looker’s modeling layer to give users access to forecasts, tie in BigQuery’s public datasets to add richness to analysis, and show off LookML’s ability to handle nested tables for faster performance on transaction analysis all through a complete end to end demo.
Maximize Efficiency in Document Extraction Models with Document AI Workbench GA Release
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Each day, more documents are created and used across companies to make decisions. However, the value in these documents is primarily expressed as unstructured data, which makes the value difficult and manually intensive to extract and use for business processes.
As the number and variety of documents used by businesses proliferate, machine learning (ML) solutions need to be more flexible to handle the broader set of use cases. That’s why we introduced the first Document AI Workbench model, Custom Document Extractor (CDE), in Public Preview at Google Cloud Next ‘22. CDE makes it fast and easy to apply ML to virtually any document-based workflow to extract structured data from unstructured document types, to automate business processes.
CDE lets developers and analysts use their own data to train models and extract fields from documents needed for the business. CDE lets organizations build models faster and with less data — thus accelerating time-to-value for processing and analysis of data in documents.
Today, we announce that Document AI Workbench is Generally Available (GA), open to all customers, ready for production use through APIs and the Google Cloud Console. Document AI Workbench is covered by the Document AI SLA — online and batch document prediction is supported with >=99.9% uptime. Furthermore, Document AI Workbench is now covered by Google Cloud’s GA product terms. For example, Google will notify customers at least 12 months before significantly modifying a customer-facing Google API in a backwards-incompatible manner.
In this blog post, we’ll explore ways customers are already using CDE and Document AI Workbench’s updated capabilities.
What users are saying about Workbench
Deliver higher model accuracy with Workbench
Users leverage Workbench to ultimately save time and money. A third party evaluated Document AI Workbench and concluded that it extracts data more accurately1 than several competing products for document types with variable layouts (e.g. invoice, receipt, bank statements, paystubs). Better accuracy drives higher automation rates, helping Workbench users save time and money.
Chris Jangareddy, managing director for Artificial Intelligence & Data at Deloitte Consulting LLP said, “Google Cloud Document AI is a leading document processing solution packed with rich features like multi-step classify and text extraction to automate sorting, classification, extraction, and quality assurance. By combining Document AI with Workbench, Google Cloud has created a forward-thinking and powerful AI platform for intelligent document processing that will allow for process transformation at an enterprise scale with predictable outcomes that can benefit businesses.”
Mansoor Khan, CEO of OneClinic said, “We help medical professionals scale their clinics through automation. We used Google’s Document AI Workbench to create a model to automatically extract data from patients’ insurance cards as part of our patient check-in software. Workbench is easy to use and we are really happy with the model accuracy — it extracts data more accurately than what we would expect from human data entry.”
Rajnish Palande, VP, Google Business Unit for BFSI, TCS said, “The Google Cloud Document AI Workbench leverages artificial intelligence (AI) to manage and glean insights from unstructured data. The Workbench brings together the power of classification, auto-annotation, page-number identification and multi-language support to help organizations rapidly deliver enhanced accuracy, improved operational efficiency, higher confidence in the information extract, and increased return on investment.”
Build production ready models faster with Workbench
Document AI Workbench helps users create machine learning models faster. For example, a third-party evaluation shows Document AI Workbench trains machine learning models up to 3x faster than a leading competitor. This is an important improvement which lowers total cost of ownership and increases value.
Dallas Dolen, Partner, Google Alliance Leader at PwC said, “Google Document AI Workbench helps to accelerate our custom parser models training as well as improves accuracy and performance using a custom document extractor with human in the loop. It helps us solve complex business problems for our clients in the financial services and healthcare industries.”
Ziang Jia, Senior DocAl Development Lead at Resultant, said, “Document AI Workbench has unlocked a brand-new machine learning development experience for information extraction solutions. Its simplicity and robustness enabled us to build models and deliver a highly accurate outcome in an agile way for a large government agency. We couldn’t be more impressed by its simplicity and robustness and are excited to see how the product will evolve in the future.”
Sean Earley, VP of Delivery Services of Zencore said, “Document AI Workbench allows us to develop highly accurate document parsing models in a matter of days. Our customers have automated tasks that formerly required significant human labor. For example, using Document AI Workbench, a team of two trained a model to split, classify and extract data from 15 document types to automate Home Mortgage Disclosure Act reporting. The mean trained model accuracy was 94%, drastically reducing the operational cost of our customer’s compliance reporting procedures.”
What’s new with Document AI Workbench
The latest Workbench capabilities make it even easier to train and deploy an extraction model:
- With Workbench’s public APIs, you can programmatically create, delete, train, evaluate and deploy models.
- Our updated dataset management tools automatically detect and create existing schema labels from your pre-annotated documents. They also provide you more flexibility when creating and managing schema.
- Our new DocAI Toolkit includes a labeled document converter so that you can easily convert your labeled documents to DocAI’s format and start training faster.
- We’ve reduced the cognitive load for labelers with efficiency enhancements to our Labeling UI.
- The revamped Processor Gallery helps you quickly identify the best model for your use case.
What’s next for Document AI Workbench
We continue to invest in Document AI Workbench to help you automate document processing. Here are a few things we’re working on that we’re excited about:
- Classify document types with the Custom Document Classifier (CDC), coming soon in public preview
- Copy processor versions across projects and processors to streamline managing development and production environments
- Support larger documents (e.g., longer than 50 pages) so you can process a wider array of documents
- Broader (non-latin) language support–equivalent to Document AI OCR
- And many more investments, using state of the art technology, to help you build world class models faster to automate document processing
Document AI Workbench is in GA and ready for production workloads. Learn more via Document AI Workbench documentation or try it out in the Google Cloud Console.
Acknowledgements: Tomas Moreno, Outbound Product Manager, Lukas Rutishauser, Software Engineering Manager, Michael Kwong, Software Engineering Manager, Rajagopal Janani, Software Engineering Manager, Michael Lanning, UX Designer.
1. When trained with 200+ documents
Contact Center AI (CCAI) with Agent Assist can Lower Opex and Handle 28% More Chats
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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.
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