The questions you are probably about to ask.

Jean is a sovereign AI operating system hosted on your own infrastructure. It connects to the systems your company already uses, builds an ontology of how your business actually works, answers questions from live data with the sources attached, and runs the repeatable work as governed workflows. Everything it accumulates stays in your environment: the ontology, the trained mates, the workflows and the audit ledger.

A dashboard shows what happened and leaves the interpretation to you. A chat wrapper answers quickly with nothing checking it. Jean reasons across your systems, grounds each answer in the records it retrieved, scores its own confidence against Synthia, passes every request through the Governance Gateway, and writes the whole exchange to a ledger. It is a system you own rather than a reporting layer or an API wrapper.

Fintech and regulated mid-market enterprises of roughly 200 to 5,000 people, where AI is a strategic decision rather than a departmental tool. The people who buy it are usually CTOs, CIOs and chief risk officers, because they are the ones accountable for what the company's AI does.

In the sense that matters, yes. Palantir sells a data operating system to governments and the largest enterprises, with a services engagement to match. Jean is built for fintech and regulated mid-market enterprises of 200 to 5,000 people: it deploys inside your own perimeter in weeks, the ontology, the trained models and the workflows become your property and a capital asset under IAS 38, and the price is set for a company that does not have a forward-deployed engineering budget.

Enterprise AI orchestration is the layer that decides which model handles which request, with which data, under which rules, and records the decision. In Jean that layer is model-independent. Every request passes the Governance Gateway first, then Simon picks the model your classification allows: an internal small model, a sovereign model inside your perimeter, or a frontier model where your policy permits it, with sensitive entities masked. Company, department and individual limits can only narrow the choice, and the reason for each routing decision is written to the ledger. That is AI model orchestration you set and can audit, rather than one vendor's model behind every answer.

They solve different problems. DataRobot is a platform for data science teams to build, deploy and monitor machine learning models, and it can run on your own infrastructure. Jean is an on-premise AI platform for the whole company: it connects to the systems you already use, builds an ontology of how your business works, answers questions from live data with the sources attached, and passes every request through a Governance Gateway that writes to a ledger you own. If you need a workbench for your data scientists, DataRobot is built for that. If you need AI the whole company can use and your risk committee can audit, that is what Jean is for.

No. Jean removes latency and assembly work: finding the numbers, reconciling them, writing the first draft, noticing the thing nobody noticed. The judgement and the accountability stay where they are. Any workflow that touches a customer, a contract or the books still stops for a named human.

The question is resolved against your ontology, the relevant records are retrieved from the systems they live in, the router picks a model for that task, and the answer comes back with its sources, its reasoning and a confidence score. Request, policy verdict, model call, sources cited and any human approval all land in the ledger as it happens.

For the work a company actually asks of it, yes, and often better. Most enterprise questions are classification, extraction, drafting inside a known template and answering from retrieved context. Small language models (SLMs), hierarchical reasoning models (HRMs) and tiny recursive models (TRMs) handle that class of work on hardware you already own, trained on synthetic data from Synthia so no confidential record is needed to tune them. Where a question genuinely needs a frontier model, the Governance Gateway routes it there under your policy, with the sensitive entities masked, and records the call.

Synthia builds entire synthetic companies from a claim graph, with the right answers planted inside them, and scores retrieval, prompts and policy adapters against that known ground truth before a single real record is connected. Either an answer matches the planted truth or it does not. Your production data is not the test set.

Jean says when it does not know, which demonstrates worse and works better. Answers carry their sources and a confidence score, so a weak answer reads as a weak answer rather than a fluent one, and anything below the threshold you set goes to a human instead of into a decision.

Yes. The ledger is hash-chained, so a missing or altered entry is detectable, and each entry holds the request, the policy verdict, the model used, the sources cited and the human who approved it. That record is what turns EU AI Act record-keeping and human oversight into a query rather than a project.

With a demo and two questions your leadership asks every week. After that there are four stages: discovery, security review, a fixed-scope pilot on one department, then rollout. Three to four months from the first call to production is typical, with the security review running alongside discovery rather than after it.

No, and deliberately so. Jean is deployed through a guided engagement, so that scope, policy and validation are settled before it touches production data. Your own engineers can run and configure it; we are there for the stages where that is harder than it looks.

You stop, and you keep what was built. The pilot is graded on questions you wrote before anything was built, with the ledger as the evidence, and the ontology, the weights and the ledger have been in your storage since its first day.