About
An applied AI engineering firm
We design, build and run AI systems inside companies, and we build AI products of our own.
Why we exist
Why AI pilots stall, and what we do about it
Most companies have tried AI by now. Many have a pilot that worked in a demo and stalled before production. The gap is rarely the model. It is the engineering around it: the integrations, the permissions, the evals, the monitoring and the people who will run the system.
That is the work we do. We design AI agents, agentic workflows and AI software factories, connect them to the systems a company already runs, measure them with evals and run them in production. Then we hand them over to the team that will own them.
We also build our own products, such as git1file, on the same stack and to the same standards. It keeps us close to the problems our clients face.
Company
Facts
- Legal name
- Nina Labs AI LLC
- Headquarters
- Hockessin, Delaware, United States
- Focus
- AI agents, agentic workflows, AI software factories
- Product
- git1file, since March 2025
- Contact
- contact@ninalabs.ai
Principles
How we work
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01
You own everything
Code, prompts, eval sets, infrastructure definitions and documentation live in your repositories and your cloud accounts from the first day.
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02
Measure before and after
Every project starts with a baseline and ends with the same measurement. If the numbers do not move, we say so.
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03
People approve what matters
Irreversible actions go through a person until the evals and the production data show the system can do them alone.
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04
No lock-in
We choose models and platforms per task and build on open protocols such as MCP and A2A, so you can switch later.
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05
Every engagement has an exit plan
From week one we write down what your team needs to run the system without us, then we work through that list.
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06
Small teams, direct contact
You work with the engineers who build your system, not with a layer of account managers.
Tell us which process you want to hand to an agent
A 30-minute call with an engineer. We will tell you whether an AI system is the right tool for it, and what it would take to run it in production.