BOLT

VeriCore was started by two certified mortgage bankers who had spent their careers inside the systems they wanted to fix. They knew the work from the inside, which meant they knew exactly where it tended to break.

When they brought BOLT to us, the brief was simple: give operations leaders a way to capture what they know and prove a system is safe before it ships.

For decades, mortgage teams validated new systems the same way. Test cases lived in spreadsheets, results were scattered across email, and there was no audit trail when a regulator came asking. It was tedious work, but at least the systems were predictable.

That predictability is gone. Today's operations mix AI, machine learning, hard-coded rules and human judgment into hybrid systems that keep changing. One unverified change can slip into production and cause real damage downstream: to borrowers, to compliance, to the business.

BOLT pulled loan data, test definitions and UAT runs into one system, with an audit trail behind every result. The old manual playbook of spreadsheets, email threads and lost context turned into a single source of truth that VeriCore's customers can hand straight to a regulator.

It's the foundation of VeriCore's mission: bringing safe AI to mortgage operations by verifying systems before they ship, with the proof and oversight the industry expects.

VeriCore came to us with a clear vision, captured in early Figma frames and a list of features. Our job was to turn that into something teams could actually run: sharpening what mattered, designing it as one coherent system and building it end to end.

You can't optimize a system into being safe after it launches. You have to verify it first. BOLT makes that the default: one platform where teams prove a hybrid system works before it ever reaches a borrower.

It rests on three layers that scale together. Loan profiles hold the test data, scenarios define what to check and UAT runs pair the two and track every step, result and sign-off through to the end.

Build your repository by hand or import it through CSV, JSON or MISMO XML. Bulk uploads preview and validate every record before anything gets saved.

Search and filter by loan type, lifecycle, purpose and more. Active filters stay visible, so finding the right loan and sharing that view with your team takes seconds.

Test definitions scoped to a workflow and loan type. Each step spells out what to verify, what to capture and what failure looks like.

Pair a loan with a scenario and you have a run. Testers work through the steps, managers review and teams rerun once fixes land.

Every loan, scenario and run rolls up here: failure rates by workflow, pass-rate trends, retest health and how the team is loaded. All of it exports for stakeholder reporting.

Four roles mirror a real mortgage QA org. Testers run the UAT tests, managers assign work and review outcomes, admins manage the team and workspace and owners hold full control.

Each person sees only what their role allows, and every action lands in an audit trail, so the proof a regulator asks for is already there. Invite people by email, update roles inline and keep an eye on pending invitations.

Their background in healthcare technology meant they arrived with a genuine understanding of regulated markets. Multi-state statutes, data privacy and audit trails were treated as foundational requirements, not secondary concerns.

They never blindly accepted instructions. They pressure-tested assumptions and protected the platform's integrity. When obstacles surfaced, the answer was never “we cannot do it,” but always a collaborative push to find the best approach.