USAN knows why your customers keep calling

The clue was 165 phone calls. A vehicle-financing company had a problem with its debit-card processing system, and customers were calling about it. According to USAN’s published case study, its AI Contact Analytics identified the cluster in near-real time. The company could then repair the problem before it spread further. Somewhere behind an unusually busy queue was a broken payment process. Listening to the queue helped locate it.

That is a useful place to begin with USAN. Contact center technology is usually sold in the language of shorter calls, smarter routing, and agents who can accomplish more before lunch. Here, the interesting result was a reason for the call disappearing. A support conversation became evidence for another department. The customer’s complaint had finally escaped the customer service department.

165 calls, one repair

The financing company had worked with USAN since 2008 and completed its Amazon Connect migration in 2024. Next came a more revealing question: what were its customers actually trying to do? USAN’s analytics, built on Amazon Bedrock, surfaced a debit-card issue among the conversations. The case describes an operational fix, rather than merely better handling of complaints about the same defect.

The distinction matters. An agent can be polite, fast, and completely unable to repair a payment system. Coaching that agent harder would leave the underlying machinery untouched. The lesson to borrow is organizational: route the insight to whoever owns the process. A support dashboard becomes more valuable when the billing team has a reason to open it.

The platform is only the beginning

USAN occupies the practical space between buying a cloud platform and having a working service operation. Amazon Connect supplies the contact center foundation. USAN designs the deployment, integrates business systems, adds its own applications, and offers ongoing management. Its expertise covers routing, self-service, outbound communications, agent workspaces, analytics, and the joins between them.

Consider Edcor, an education benefits administrator. Its premises-based phone and voicemail system had become too restrictive. USAN implemented Amazon Connect, Amazon Lex chatbots, Contact Suite, and custom back-office integrations. Agents gained information about incoming interactions, while customers gained a choice of channels. The first failure was architectural: the existing system could not comfortably support the data access and connections the business needed.

This is why a migration is more than moving a telephone number. A caller’s history, the routing decision, and the employee’s next screen all need to agree about what is happening. Otherwise, a modern phone system can deliver a customer to an agent who still has to start from scratch. The cloud has arrived; the context is running late.

Eight calls are a very small window

Dominion Energy Virginia supplies another view of the problem. Its USAN case study describes a change from manually scoring eight calls per agent each month to using generative AI across all agent-handled calls. That changes the coverage of the listening exercise. It does not, by itself, establish that every automated assessment is correct.

The utility wanted clearer information about customer pain points, contact volume, and agent performance. USAN’s analysis identifies intent trends and coaching opportunities. The important managerial move is to look for patterns and outliers, then investigate. A larger collection of observations can reveal something a small monthly sample never encounters.

“The true value of meaningful, shared data cannot be overstated.”Utibe Bassey, VP, Customer Experience, Dominion Energy

AI Contact Analytics also has a deliberately practical selling point: it discovers intent categories rather than requiring the customer to define every category beforehand. USAN positions this as a way to uncover problems operators did not know to search for. The payment-processing example makes the claim intelligible. An unexpected pattern has to be allowed to appear before anyone can name it.

The bot needs a supervisor, too

USAN’s AI Contact Explorer tackles a related problem in automated service. It is designed to audit voice and digital bot conversations, flagging hallucinations, process failures, frustrating exchanges, and transfers to people. Its advertised coverage is every bot interaction. The point is ongoing inspection: what happened after the demonstration became a customer-facing service?

The illustration is almost comically mundane. The bot asks again for a Social Security number after the customer has already corrected it. Nothing spectacular has happened. The conversation simply refuses to move forward. That is precisely the sort of irritation an operator needs to see, assign, and repair.

USAN’s proposed loop runs from analysis to detection, prioritization, tuning, and validation. For a buyer, the sensible question is who owns each step. A team that collects failure reports but cannot change its integrations, prompts, or business rules will collect the same reports again. Successful automation needs access to the systems that complete the customer’s task, plus a usable route to a person when the task exceeds the bot’s remit.

The bill includes the morning after

The commercial model combines professional services, software, and continued operations. Realm, launched in January 2024, was introduced with consumption-based pricing and four components: Agent, CX Manager, Intelligence, and Campaign. These cover the workspace, supervision, insight, and outbound activity around Amazon Connect. USAN also sells implementation packages through AWS Marketplace, where migration pricing is tailored through private offers.

The 90-day package includes incident and defect handling, up to five configuration changes, and an AWS spend review. Longer-term managed services add monitoring and account management, with pricing linked to Amazon consumption. Buyers should scope the work around their channels, integrations, testing, and support needs. A starting price is useful precisely when its boundary is clear.

A specialist in a crowded room

Genesys, NICE CXone, and Five9 are platform alternatives in the contact center market. USAN’s role is different in purchasing terms: it extends and implements a platform, particularly Amazon Connect. Its analytics can also work across other platforms. One published fintech case describes Genesys handling voice while Amazon provides IVR and self-service. The analytics project did not wait for a complete migration.

For enterprises in utilities, finance, healthcare, retail, and travel, this mix of software and services offers a way to buy specialist execution alongside technology. OpenLoop’s CTO, Curtis Olson, put the implementation value plainly:

“USAN really understood an ecosystem we were totally unfamiliar with with Connect.”Curtis Olson, CTO, OpenLoop Health

That positioning has acquired more institutional backing. USAN announced a Zendesk partnership in September 2025, AWS Generative AI Competency in November, and a strategic collaboration agreement with AWS in March 2026. These are evidence of partner relationships and recognized expertise. The operational test still happens inside the customer’s own workflows.

The most portable part of USAN’s approach requires no particular vendor. Start with why people contact you. Look beyond the categories you already recognize. Check whether the task was completed. Give the emerging problem to a team that can change the underlying process. Then inspect the next batch of conversations for evidence that the repair worked.

USAN’s appeal is clearest where a business has complex systems, substantial contact volume, and an operational team prepared to act on what it learns. Without reliable context or authority to repair the process, an elegant dashboard can become an expensive spectator. Those 165 calls make the alternative wonderfully concrete: hear the pattern, find the fault, and spare the next customer the conversation.