Forward Deployed AI Engineer

Forward-deployed, and still there in month eighteen.

Keelo does not hand off documentation and leave. Engineers deploy into your environment, sit close to the people doing the work, and stay through the part where the system has to keep running.

Talk to an engineer

[01]How we work

The difference from a consultancy is structural, not cultural.

A consultancy's economics require a person on every problem, so its incentive is to keep people on problems. Keelo's economics require the platform to absorb the repeatable work, so its incentive is to remove people from every step that does not need judgment.

Engineers stay where judgment pays: architecture, exception design, approval surfaces, and the merge button on the self-healing loop.

Engineers on site, in your systems

The people who design the agent are the people who watch it run. Nobody hands a specification to a delivery team that has never seen the work.

A small number of deployments

The work has to matter — to the business, to the people doing it, and to Keelo. Pilots that exist to be presented internally are declined.

The platform absorbs the repeatable part

Everything a human does twice becomes a candidate for the factory to do. That is the arc, and it is measured in what percentage of each new deployment is already built.

[02]How an engagement runs

One workflow first. Then the fleet.

[01]
Point it at one workflow
Discovery runs against a scoped set of real work inside your environment and comes back with the workflows it found, ranked by how automatable they are and what they cost you today. You see the build queue before you commit to building anything on it.
[02]
The first agent goes live
The top-ranked workflow gets designed, built, and shipped into the system your team already uses, with the approval surface designed for the people who will actually use it. It passes its eval gate before it goes live, not after.
[03]
The fleet takes over
Keelo watches every run, catches the failures, writes the fixes, and opens the pull requests. The next workflow off the queue starts with everything the first one built.

[03]The team

AI researchers and engineers who have already done this inside someone else's business.

Keelo is built by a small team of AI researchers and AI engineers who have worked as forward-deployed engineers inside enterprises — in their systems, against their real data, on the hook for what happens when the agent runs and nobody is watching.

That is where the platform comes from. Evals written before the feature, deterministic steps preferred over reasoned ones, approval surfaces designed rather than bolted on, observability from the first run — every one of those is a rule somebody here learned the expensive way, on a real deployment, in front of a customer.

Research that had to survive a Tuesday

Everyone here has shipped a model into a workflow somebody depends on. That is a different discipline from making a benchmark number move, and it is the one this platform is built out of.

Forward-deployed by default

The team works inside the business — in its systems, against its real data, close to the people whose job is being encoded. Nobody on this team has only ever seen the work described in a slide.

Deliberately small

The platform is supposed to absorb the repeatable work, which means headcount is not how Keelo scales. The team grows when the work genuinely needs judgment that does not yet exist here.

The person who scopes your first workflow is the person who builds it and the person who is still there when it breaks.

[04]Careers

Engineers who ship into someone else's production environment.

You write the eval before the feature, you reach for the deterministic solution first, and you consider an agent unfinished until it has run unattended for a month. An unfamiliar ERP and an undocumented schema do not slow you down.

Tell us what you have shipped

No formal openings are listed. Keelo hires when it meets the right engineer, which is often before there is a posting.

Get started

Bring the workflow that matters too much to leave as a prompt experiment.

The first conversation is about which workflow is worth encoding, what has to stay human, what has to be governed, and what system needs to exist around the model. It is a technical conversation with the person who will build the thing.

Keelo takes on a small number of deployments. The work has to matter — to the business, to the people doing it, and to Keelo.