Company
Keelo is the autonomous agent factory for enterprise.
You deploy it once. It then finds the work worth automating, builds the agents for it, ships them into the systems your team already uses, and keeps them running — without being asked, and getting cheaper every time it does.
[01]Where Keelo is going
A proven operator today. A platform on a defined arc.
Today
A proven operator
Keelo runs agentic systems in production inside enterprise environments. Real workflows, real revenue, clean IP, and clean licenses. The factory described on this site is not a roadmap. It is what ships those systems now.
The arc
Progressive autonomy
Every stage of building an agent becomes something the system does itself. Discovery, architecture, code generation, deployment, repair, and improvement each move from a person doing the work to the platform doing it and a person approving it. Forward-deployed engineers stay in the loop, but only where judgment pays.
The destination
A managed fleet
Any repetitive enterprise workflow becomes a managed, self-improving agent. The enterprise stops buying automation projects one at a time and starts operating a fleet with an operating cost that does not scale with its size.
[02]The thesis
Building an agent is easy now. Keeping a fleet of them alive is the hard part.
Anyone can prompt a model into a demo. That capability is broadly distributed and it is getting cheaper every quarter. It is not a business.
The unsolved problem is the rest of the lifecycle. Discovering what is actually worth automating, rather than what leadership assumes. Building to a standard that survives real data. Deploying into a system of record without breaking it. And then keeping the thing healthy for years while templates change, vendors change their formats, schemas drift, and the model underneath gets deprecated.
The market solved “make a demo.” Keelo operates everything after it.
What makes that a company rather than a services firm is where the work accumulates. Every deployment writes back into one control plane — the Keelo Brain — which holds the reusable machinery, the engineering knowledge behind it, and the live state of every agent running. The next one starts from there rather than from nothing.
The moat is not the model. It is everything around it.
[03]Why now
Three curves crossed, and the constraint moved.
01
Agents crossed from demo to dependable
For structured, judgment-light enterprise work, model reliability is now good enough to trust in production — with the right harness around it. The harness is the condition. Without evals, guardrails, deterministic fallbacks, and an approval surface, the same model that passes a demo fails a Tuesday.
02
The economics finally work
Value-tier model costs fell roughly 80% year over year. Work that was uneconomic to automate in 2024 is routine to automate in 2026. The threshold for “worth building an agent for” has dropped by an order of magnitude, and it keeps dropping.
03
The knowledge is still in people, not systems
Workflow logic, vendor quirks, exception handling, and house taxonomy live in a handful of heads. None of it is written down. The window to encode that knowledge into a system is open right now, and it closes one retirement and one resignation at a time.
The constraint is no longer capability. It is having a system that turns capability into operating leverage, repeatably.
[04]Principles
Seven rules. Every agent is built to all of them.
[01]
Agents propose, people approve.
Nothing reaches a system of record without a person signing off. This is the control that makes every other claim on this site defensible.
[02]
You own the IP and the cloud.
Agents deploy into your environment. Data stays where it already lives. You own the code and the workflow logic encoded in it. Permissive open-source licenses only.
[03]
Reusability is the scoring function.
Every component is evaluated on whether the next agent can inherit it. Work that cannot be reused needs a specific reason to exist.
[04]
Deterministic before reasoning.
If a rule can express it, a rule expresses it. Models are used where the input genuinely varies. This is a reliability decision and a cost decision at the same time.
[05]
Evals are not optional.
Every output class is graded against golden and holdout sets before release. An agent that has not been measured has not been built.
[06]
Observability first.
Every agent streams structured, per-run logs from day one. An agent that cannot be observed cannot be operated, and cannot be repaired by the loop.
[07]
Fail safe, never silent.
When an agent is uncertain, it escalates. It does not guess, and it does not quietly degrade.
[05]How we work
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.
The distinction from a consultancy is structural rather than 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.
[06]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.
[07]Facts
- Entity
- Keelo AI LLC, a Delaware limited liability company
- What it does
- Designs, builds, deploys, and operates production agentic systems for enterprise
- Deployment model
- Inside your cloud and your accounts
- IP position
- You own the agent and its workflow logic. Permissive open-source licenses only.
- Team
- AI researchers and engineers who have been forward-deployed inside enterprises
- Engagement model
- Forward-deployed engineering, small number of concurrent deployments
- Contact
- keelo.ai/contact
[08]Careers
Engineers who ship into someone else's production environment.
Keelo hires a specific kind of engineer: the one who is comfortable in an unfamiliar enterprise stack, who writes the eval before the feature, and who considers an agent unfinished until it has run unattended for a month.
The work is forward-deployed. You will sit close to the people whose job you are encoding, read their actual files, and be responsible for the system when a vendor changes a template at 6pm. In exchange, everything you build once is written back to the Brain and gets reused, which means you are compounding rather than repeating.
You ship to production, not to a notebook.
Deployment, monitoring, and the on-call consequences are part of the job you consider yours.
You write the eval first.
You are uncomfortable shipping an output class you cannot measure, and you say so.
You reach for the deterministic solution.
You use a model where the input genuinely varies, and a rule everywhere else.
You are good in someone else's environment.
Unfamiliar ERPs, undocumented schemas, and a stakeholder who has ten minutes do not slow you down.
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.
Direct: Edward@keelo.ai