I founded Density Labs in 2016 and I still run it. It's the AI Engineering Partner for mid-market US companies, the firm that gets a stuck AI roadmap out of the notebook and into production. This page is the short version. The whole story, the method, the pricing, and the case studies live on the company's own site.
Density Labs · distributed engineering since 2016 · 96% client retention
Who it's for
Mid-market US companies, $50M to $500M ARR, with an AI roadmap stuck in pilot. The pattern is almost always the same: a model that works in a demo but can't get to production, and a board asking why the AI line item still hasn't shipped. The gap is rarely the model. It's data readiness, integration risk, and a roadmap that was never built for production in the first place.
Density has run distributed, cross-border engineering since 2016, with 96% client retention, against an industry average closer to 75%. That number is the whole argument. Teams don't stay for a decade unless the work actually ships.
The system underneath it is what I call Invisible Distance: the operating model that lets our teams ship like they're in the room even when the timezones are spread. It's the thesis of my book, The Invisible Distance, and it's the reason a roadmap we hand a client is one we can actually execute, not a slide deck.
AI engineering doesn't lower the bar for distributed work. It raises it. Evals, retrieval, model behavior, ops: every layer has more degrees of freedom than the last generation of software. The teams that ship it are the teams that already had the system. That's the bet Density is built on.
Two ways in, and they connect. Start with an assessment, then turn it into delivery with no restart. The full detail, pricing, and intake all live on densitylabs.io.
A fixed-scope, two-week assessment that ends in one decision-ready deliverable: three scored pillars (data readiness, AI-fit, integration risk), a clear verdict, a readiness scorecard, a prioritized production roadmap, and the recommended first build.
Best fit: you need to know exactly where your AI roadmap stands and what to build first, before committing to a full engagement.
When you're ready to ship, the Diagnose roadmap becomes the build plan. An embedded AI engineer, or a full cross-functional AI squad, works in your repo, on your roadmap, in your standup, and owns the production work that decides whether AI ships or just demos.
Best fit: an AI initiative that has stopped being one feature and started being a product line, or a team with the demand but not the headcount.
The method, the case studies, the pricing, and the way in are all on the company's site.