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

Before investing in a full build, Summit Trails used a focused four-week feasibility study to prove that local vision models could read on-screen work at 98% accuracy without sending a single image to the cloud.

98%

Screen-reading accuracy validated

4–8 weeks

Implementation Time

Nitesh Pant

Co-founder

DevDash Labs

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The Challenge

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.

What They Built

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.

AI Role

Local Visual Language Models read on-screen work directly from screenshots, with dual agentic workflows cross-checking each other to push accuracy toward the 98% trust threshold. All image processing stays inside the private cloud; an optional cloud layer validates results without exposing raw images.

Infrastructure

Client-controlled private cloud (all image processing on-premise) • On-premise compute for local vision inference; only sanitized text metadata leaves the environment • Phased architecture spanning eight integrated systems

Integration Points

Screen/screenshot capture pipeline feeding the local vision models • Sanitized text-metadata egress to an optional cloud validation layer • Eight integrated systems staged behind Go/No-Go build gates

Impact

98% accuracy validated

The four-week study confirmed that 98% screen-reading accuracy — the trust threshold supervisors required — was achievable.

3 CIOs approved architecture

Three independent CIOs signed off on the zero-image-to-cloud, privacy-first architecture designed for regulated industries.

20-week roadmap, 8 systems

DevDash delivered a de-risked, three-phase 20-week build plan with Go/No-Go gates spanning eight integrated systems.

Technology Utilized

Implementation Complexity

Risk & Compliance

Cost Reduction

Best Fit For

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.

Nitesh Pant

Co-founder

DevDash Labs

Co-founder and COO of DevDash Labs, an applied AI research and development company that builds products to solve the hardest problems for SMBs looking to adopt AI and automate their processes.

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Industry:

Professional Services

Business Function:

Operations

Company Size:

Project Cost:

Cost not disclosed

Ownership:

Organization Type:

Private Company

AI Pattern:

Computer Vision

AI-Accelerated Custom Software

Value Type:

Risk & Compliance

Cost Reduction

AI Model:

Frequently Asked Questions

What did the Summit Trails feasibility study set out to prove?

Whether local AI vision models could read on-screen work at 98% accuracy — the threshold supervisors required — without sending any images to the cloud, before Summit Trails funded a full build.

How did DevDash Labs keep sensitive data private?

All image processing ran on local Visual Language Models inside the client's private cloud. Only sanitized text metadata ever left the environment, so no PII or PHI from credit-union, insurance, or healthcare customers was exposed.

What accuracy did the study achieve?

The four-week study validated that 98% screen-reading accuracy was achievable using dual agentic workflows, with an optional cloud validation layer to push accuracy further.

What did Summit Trails receive at the end of the engagement?

A de-risked, three-phase 20-week build roadmap with Go/No-Go gates spanning eight integrated systems, plus an architecture pressure-tested and approved by three independent CIOs.

Who is this feasibility approach best suited for?

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

Originally published by

DevDash Labs