Summit Trails Validated 98% Screen-Reading Accuracy Before Writing Code

Summit Trails' founders wanted to eliminate manual implementation work and costly custom integrations by having AI observe work directly from screenshots. Supervisors would only trust the data at 98% accuracy or better, and target customers in credit unions, insurance, and healthcare demanded that no PII or PHI ever leave their environment. The team needed to know whether those constraints were technically feasible before funding a build.

DevDash Labs designed a visual intelligence platform built on local Visual Language Models running inside the client's private cloud, so images are processed on-premise and only sanitized text metadata ever leaves. Dual agentic workflows were used to push accuracy toward the 98% threshold, with an optional cloud validation layer. The design spanned eight integrated systems.

The engagement was structured as a four-week 'Step Zero' feasibility study. Weeks one and two covered deep discovery, weeks two to three produced the architecture and the pivot to a privacy-first design, and weeks three to four delivered a phased roadmap. The final architecture was pressure-tested with three independent CIOs before any build began.

Best fit for founders or teams weighing a significant AI build in a privacy-sensitive, regulated setting who want to de-risk feasibility and architecture before committing capital.