Scaling a computer vision safety platform with an embedded team

Groundlight is a US-based computer vision SaaS for industrial safety. The core product is an API that lets customers ask natural-language questions about an image or video stream (is a hard hat on, is the safety guard in place, is a door open) and get an immediate answer back, with human reviewers in the loop when the model is uncertain. The product applies across manufacturing, facilities, and any environment where machine vision combined with human verification shortens time-to-detection.

As Groundlight grew from an MVP into a SaaS product, the bottleneck was engineering bandwidth on both the frontend and the backend. The customer-facing interface needed real product work (managing detectors, viewing detector improvement over time, surfacing labeler performance reports), and the in-house team did not have headcount to ship at that pace.

Groundlight found Leanware through a Google search. The brief was straightforward: extend the internal engineering team with frontend and backend developers who could be trusted to work on the production system without heavy hand-holding.

The engagement is dedicated-team capacity, retained on a monthly basis. The team has been embedded with Groundlight since December 2022: one senior full-stack engineer, three mid-level full-stack engineers, and a product designer. AI fluency is a baseline on the team rather than a specialty rate, which matters here because Groundlight's product is a computer vision system and the engineering work routinely touches the ML feedback loop. The point of contact rotates between engineers depending on which part of the system is shipping that sprint.

The work spans the production system. On the frontend, the team owns the customer-facing detector management UI in React Native, which is the interface a customer uses to create a detector, upload images, label new examples, and watch detector accuracy improve over time. On the backend, the team works in Django and Python, integrating with the labeling teams that supply human-in-the-loop answers when the computer vision model is uncertain about a frame.

A representative thread of work the team has shipped: when the algorithm produces an incorrect or low-confidence answer, the system routes the image to a labeling reviewer; the reviewer response is then incorporated into the detector training loop. Building that loop reliably (labeler queueing, reviewer feedback ingestion, accuracy-over-time visualization) required engineering across the stack and ongoing iteration with the data team.

The team has also shipped image bounding-box authoring (so customers can mark regions of interest before training a detector), per-customer account metrics, signup flows, and a series of UI/UX improvements aimed at manufacturing operators rather than the engineering audience the early product was shaped for. Tech stack: React Native, Django, Python, PostgreSQL, AWS, Kubernetes, GitHub Actions.

Communication runs on Slack and Jira. Code goes through the Groundlight internal review process; the team works increasingly independently as code quality has held over time.

Three years in and counting. The frontend went from intermittent shipping to substantial launches every two to three weeks, and the engagement has expanded from a frontend trial into full-stack ownership over multiple parts of the product. Code-quality review has held to the point where the team now works increasingly independently. The Clutch review came in at 5.0 out of 5 across every category, with $120,000 invested at that point.

The qualitative thread that runs through the relationship is trust. Morgan Venable, Head of Product at Groundlight, has been on record describing the engagement as "so much more" than an outsourcing partnership and noting the senior engineers are "always around to support us." Decision input flows in both directions. Groundlight respects the technical input the team brings, and the team takes ownership of code quality and direction without close management.