Priage connects directly to the core banking systems community banks and credit unions actually run — Symitar, DNA, Corelation, FIS — and adds agentic AI for lending, servicing, and fraud workflows. No core conversion, no rip-and-replace, and no record ever leaves your compliance boundary.
Most AI vendors list "100+ core integrations" as a feature. In practice that usually means a thin, generic connector. Priage takes the opposite approach: real support for the transaction codes, teller workflows, and loan servicing operations the cores community banks and credit unions actually run process every day — not a lowest-common-denominator abstraction stretched across systems it barely touches.
Not "financial services" as an abstraction — the actual people who'd use this day to day, the specialists who'd oversee it, and the executives who'd sponsor it.
Hover to see how each workflow executes and the ROI impact.
Priage isn't a single chatbot bolted onto your core — it's an orchestration layer that any number of purpose-built agents run on top of, all sharing the same core-system adapters, the same compliance guardrails, and the same audit trail.
Two published industry benchmarks anchor this, applied to a worked example sized like a real credit union. Treat the example as a starting model to rebuild with your own numbers, not a promise.
Cost-per-contact and Tier-1 automation-rate figures are published call-center and AI-support industry benchmarks, applied directionally to a credit union contact center. Monthly contact volume is an illustrative assumption for a $1.5B-asset institution, sized against publicly reported credit unions in that range (e.g. TruMark Financial, KEMBA Financial) for scale only — not data from either institution. Replace the volume and mix with your own for an accurate number.
Banks and credit unions don't struggle with GenAI because the models aren't good enough. They struggle because the deployment model assumes account data can leave the institution. What that leaves you with:
Built around the constraint that account and member data never leave the core processing environment you already operate within.
This is the first workflow we're building toward with design partners — a concrete example of how the pieces work together. Watch it work step by step →
The same operating system that runs a service agent can run role-based agents that take on defined functions inside the institution — not replacing the people who own these roles, but extending what they can cover, with every action still inside your compliance boundary.
An illustrative example of the target workflow — not a claim about a live deployment today. The agent recommends; a human analyst approves before anything is applied.
Compliance isn't a layer added on top of the framework — it's the boundary the framework is built inside of.
A 30-minute call with our team — no sales deck, just your current core, your compliance constraints, and whether we're actually a fit.