interface.ai wants to end the handoff

At Affidian Credit Union, the new employee assistant had a small problem. It could help staff find answers, but first they had to open it. Christine Wiley, the credit union’s president and CEO, described the obstacle with admirable economy: “but it was another link.” In a business selling artificial intelligence, that is a wonderfully ordinary thing to go wrong.

Once Affidian integrated the assistant into its Jack Henry environment, Wiley reported a dramatic improvement in adoption. Employees could consult it where they were already working. The intelligence had become easier to reach. This is a useful starting point for understanding interface.ai: the distance between an impressive demonstration and a useful banking tool can be one browser tab.

A voice needs somewhere to go

interface.ai builds conversational software for community banks and credit unions. A customer can call, type into a website or use a mobile channel. An employee can ask about an internal procedure. Behind these encounters sits a banking platform connecting institutional knowledge, authentication, transaction rules and core systems. The buyer is the financial institution; the person asking about a balance is its customer or member.

Consider the difference between explaining a transfer and making one. The first requires an answer. The second requires verified identity, access to accounts, permission to move money and a record of what happened. interface.ai’s platform combines language models with rule-based workflows. Its proposition depends on those less theatrical ingredients: integrations, limits and controls that let a conversation produce a banking action.

The product family follows the people doing the asking. Voice AI handles phone interactions. Chat AI serves digital channels. Employee AI helps frontline staff retrieve institutional information and assist members. Assemble is the control center for reviewing knowledge, testing changes and promoting them into production. A shared platform is intended to keep these channels from becoming separate islands of answers.

The small-bank bet

Srinivas Njay and Bruce Kim launched interface.ai in 2019, following earlier work under the Payjo name. Njay’s interest in community finance has a family connection: his father ran an Indian credit union. Kim brought experience founding customer-management and billing company Inovaware. Together they chose customers for whom building a banking AI operation internally would be a substantial undertaking.

The early financing was unusually personal. Forbes reported in March 2024 that the founders had used $1 million of their savings, a $3 million credit line from the institution run by Njay’s father, and revenue. More than 100 financial institutions had signed up. Njay was still entertaining investors with the air of someone who could afford to keep talking.

By October, the company had accepted outside capital. The announced package totaled $30 million, split between $20 million in equity and $10 million in debt, with Avataar Venture Partners leading. That distinction matters: debt must be repaid. The company said the capital would support product development, customer success and expansion. It had customers before it had a venture-financing headline.

This is an enterprise software sale, supported by implementation and ongoing services. For a buyer, a useful cost calculation includes integration work, knowledge maintenance, staff training and the human exceptions left behind. A high automation percentage can still disappoint if the remaining calls take longer or the institution must maintain another disconnected system.

Olive has office hours, too

At Great Lakes Credit Union, the assistant is called Olive. A January 2025 company guide describes a legacy push-button system resolving 25% of calls without human intervention. With Olive, the reported average reached 60% during business hours and 75% after hours. The hours belong in the story. Rolling those outcomes into one triumphant percentage would conceal the conditions under which they occurred.

Radiant Credit Union offers another perspective. In a customer interview hosted by interface.ai, contact-center leader Eric Whisler described replacing aging phone banking, involving associates in testing and planning staffing around self-service. Radiant reported growing from $500 million to $800 million in assets without adding contact-center staff. That does not establish that AI caused the growth; it describes how service capacity accompanied it.

The interview’s telling detail concerns an interruption. When the assistant, Ray, became unavailable, members asked where it was. People who had reasons to distrust automation had also discovered a reason to miss it. Staff participation in testing helped build that trust. A sensible rollout gives employees enough familiarity to explain the system when a member gets stuck.

Send the question, keep the conversation

Nexus, announced in April 2026 and currently labeled beta, pushes the company’s argument further. Conventional automation eventually encounters something it cannot approve. A whole call then moves to a person. Nexus is designed to keep AI conducting the conversation while sending a specific judgment request to an appropriately skilled employee. A person supplies the decision; the assistant returns it to the caller.

The launch also described a copilot mode for human-led interactions, including document collection and application support. These are product promises to test in deployment. They should not be confused with the older customer results. February’s Telarus partnership adds another route: combining the banking platform with existing contact-center infrastructure rather than requiring every institution to start over.

The useful test is an awkward request

Banking specialization alone does not settle the contest. Posh also offers banking voice, digital and employee agents; Eltropy combines AI with financial-institution communications. interface.ai’s case rests on how its shared platform, authentication and integrations behave together. Buyers should compare an entire request, including its exception, rather than judge a pleasant voice in isolation.

The practical lesson is copyable. Start with a specific member task. Bring frontline employees into testing. Place the tool inside their workflow. Measure completed requests alongside escalation quality. Where knowledge is stale, core access is missing or employees cannot resolve exceptions, fluent conversation cannot supply the missing operation. Banking AI earns its place when the member’s job gets done.