About Casteo
AI should not require an AI team.
Casteo builds the layer between machine learning and the companies that need it — so that using AI in your business does not start with hiring researchers, renting GPUs, or standing up a data platform.
The gap we work on
The techniques that make machine learning useful — good features, reinforcement learning, agentic workflows — are well understood. Getting them into a working business is still the hard part. The cost is rarely the model itself. It is the pipeline around it: the data engineering, the training loop, the serving infrastructure, the people who have to keep all of it alive.
That cost is why AI has concentrated in companies that were already technical. A team with a clear, valuable use case but no ML function is told the answer is to build one. For most companies that is not a realistic answer, and the use case simply goes unbuilt.
Casteo exists to remove that prerequisite. We take the parts that require specialist knowledge and continuous maintenance, run them ourselves, and expose what is left as something you can call, buy, or switch on.
What we build
Features as a Service
Machine-learning features computed on demand. You name the feature over the API, we calculate it on our own infrastructure, and you get the result back. Features can also be published and sold to other users.
Deep RL Service
Deep reinforcement learning applied to real decisions and workflows, ready to use out of the box — without a research loop to run or a training environment to maintain.
Agentic AI & Tailored LLMs
Custom agentic workflows and LLM services for teams without in-house AI expertise — built around your process rather than requiring you to rebuild around the model.
How we work
Outcomes, not infrastructure.
If a capability requires you to provision, monitor, or scale something, we have not finished our job. Nothing to install, nothing to host.
Priced to be started.
You pay for outcomes, not GPU clusters. The point of the platform is that a first use case does not need a budget case.
Built for non-AI-native teams.
Our default user is an expert in their own domain, not in ours. The technical complexity is ours to absorb, not theirs to learn.
Meet the team
Every member of the Casteo team is an AI agent, not a person. That is the product working on itself — the same agentic platform we sell is what runs Casteo. Each one owns a part of the platform.
Bule
Feature Engineering AI agent
Computes and maintains the features behind Features as a Service. When you request a feature by name, Bule resolves the request, runs the computation, and returns the result.
Every feature Casteo serves passes through it.
Loop
Experimentation AI agent
Runs the training loops behind our Deep RL Service — proposing policies, testing them against the environment, and keeping only what measurably works.
Named for what it does: propose, run, measure, repeat.
Gauge
Evaluation AI agent
Measures. Benchmarks feature and model quality, watches for drift, and flags when something that used to work has quietly stopped working.
Named for what it does: nothing ships until it has been measured.
Kindi
Orchestration AI agent
Routes work across the other agents and drives our tailored LLM workflows, turning a request in plain language into the sequence of steps that answers it.
Named for al-Kindi, who invented frequency analysis — statistics, centuries early.
LJ Lemieux
Founder Human
The person behind Casteo, and the one accountable for what the agents above do.
Talk to us
If you have a use case in mind and no idea whether it is realistic, that is a good conversation to have with us.