Your model works in the notebook — it still isn't in front of customers. The gap is almost never the model. It's data readiness, integration risk, and a roadmap that was never built for production. The AI Diagnose tells you exactly where you stand and what to build first.
Density Labs · distributed engineering since 2016 · 96% client retention
Who it's for
For mid-market US companies, $50M–$500M ARR, with an AI roadmap stuck in pilot. If you've got a model that works in a demo but can't get to production — or a board asking why the AI line item still hasn't shipped — start here.
A fixed-scope, two-week engagement that ends in one decision-ready deliverable:
$2,500 fixed. Two weeks. One deliverable.
Density Labs has run distributed, cross-border engineering since 2016, with 96% client retention. Our Invisible Distance operating model is the system underneath that number — it's why our teams ship like they're in the room.
It's also why the roadmap the Diagnose hands you is one we can actually execute, not a slide deck. The Invisible Distance — Federico Ramallo's book on what holds a distributed engineering team together when the work is hard and the timezones are spread — is the thesis. The 96% retention since 2016 is the proof.
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.
The Diagnose is the front door. When you're ready to build, it converts straight into delivery — your Diagnose roadmap becomes the build plan. No restart, no re-scoping.
One senior AI engineer, embedded with your team. Owns AI features end-to-end: model selection, retrieval, evals, the production work that decides whether AI ships or just demos. Reports into your tech lead, writes in your codebase, joins your standup.
Best fit: a single AI surface that needs a real owner. RAG, agent workflows, copilots, fine-tuning, eval pipelines.
A full cross-functional AI delivery pod: tech lead, AI engineers, and a product engineer who keeps the front-of-house shipping. Same embedding model as the solo engineer: your roadmap, your repo, your standup, at squad density.
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 to take it on alone.
Detailed case studies populate as recent engagements clear permission. In the meantime, the Diagnose surfaces the closest analog to your situation directly.
Pattern of work today: mid-market US companies, $50M–$500M ARR, with AI roadmaps stuck in pilot. Embedded AI engineers and AI squads on roadmaps the in-house team is already running. Average engagement length, more than two years.
Two weeks. One decision-ready deliverable. A clear verdict on what to build first.