AI vision stations at the human touch-points of regulated, compliance-dependent, high-mix production

What it is: Most quality-critical work is qualified by inspecting the result, not the act. Rapta instruments the act — and does not care what performs it. People, machines and increasingly AI execute the steps that decide quality, and none are independently verified where the work happens.

Our core: regulated, compliance-dependent, high-mix

Under regulation, an unrecorded judgment is a liability. High mix — varied geometry, short runs, constant changeover — defeats fixed automation and one-part-at-a-time vision. Rapta configures by region of interest, not by part, so one station handles mixed product without a re-teach per variant. Instrument — The station observes the operation as performed, by operator, machine or AI agent, and measures it: size and size distribution, shape, color, surface condition, object-to-background ratio, assembly and torque. Objective, repeatable, recordable values instead of subjective judgment, in seconds not hours.

Guide — Where a person executes, the station coaches them through the sequence and confirms each step before the next is permitted: poka-yoke at the operation. Rapta augments the operator; it does not replace them. Where a machine or AI agent executes, the same loop becomes continuous monitoring and alerting. As more of the line automates, something still has to verify the automation. Capture and transfer expertise — The domain expert’s standard is captured once, at the station, and applied on every shift after, with deviation recorded as proficiency data. Expertise stops leaving at retirement and stops bottlenecking on one person. Recover — Deviation is caught at the operation that caused it, not at final inspection or the customer, and the unit is routed to rework not scrap.

Verify at source — Every judgment is tied to operation, executor, time and the conditions behind it, captured as real-time digital evidence rather than reconstructed after the fact. That is decision chain of custody: an attributable, defensible record of who or what decided, on what basis, when.

Trust and governance — Verification sits outside the system it verifies: no operator, model or machine grades its own work. The human stays in the loop by design. Evidence is captured at source and retained, so an audit reads the record rather than a reconstruction. Inference runs locally on an edge PC and no imagery leaves the plant, so data stays under your control.

Results across deployments 90% of escapes prevented at source · 20%+ recovered yield · 95% faster inspection · 80% faster time-to-competence · 30% more capacity · 3–5× ramp acceleration

Maturity 30+ customer deployments, with enterprise expansions in process. Built for production, not the bench: uncontrolled lighting, difficult surfaces, closed networks. Fielded COTS hardware and software, eight-week lead time — configuration and qualification, not development. TRL 8–9, in production at major aerospace and defense primes.

Who we work with — manufacturers in regulated, compliance-dependent, high-mix production who need independent assurance the work was done right, whoever or whatever did it.

How we start — Rapta is a fielded product, not a research program. Usual entry is a virtual live demo, an optional paid proof-of-concept on your own parts against your own pass/fail standard, converting to a line-side station and rollout.

Interactive demo: https://app.supademo.com/showcase/cmt6h4puh06m6yv0jv80upgq1

Trisha Fish · tfish@rapta.ai · https://rapta.ai/