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Point Oliver at a source and a destination. He maps your scattered data onto one canonical model, gates every record, and won't stop until it's delivered, AI-ready, with a provable trail. Built for lean teams, not no team.
Free · no sign-up · about two minutes · your experts stay in the loop
Oliver connects the systems you already run and converges them into a single model your AI can trust, with a record of every field it moved.
Each layer is a specialist agent. Oliver orchestrates all four, then writes what each one learned back to the ontology, so the system compounds with every run.
Vera connects to databases, APIs, files, SaaS platforms, and ERPs. She profiles the schema, detects incremental watermarks, and lands raw data in the lake, or raises a PR with the connector skill for review.
Most teams already own the data they need. Getting it into a state an agent can trust is the hard part, and it's where timelines slip, budgets go, and projects quietly die.
Before a single model runs, most of the budget is spent cleaning, mapping, and reconciling data by hand.
A CRM nobody finished migrating, a warehouse from three acquisitions ago, a vendor API that speaks its own dialect.
The rules that reconcile it live with a few senior people, and leave when they do. None of it is written down for AI to use.
Hiring a data team to fix it is slow and expensive, if you can hire data engineers at all. Oliver turns a department-sized problem into a conversation. Built for the teams that own the use case, not the pipeline.
Agents decide what to do; the runtime does it provably. Three guarantees are produced by construction on every run.
Source → transform → validation → delivery, recorded automatically for every record. Nothing is a black box.
Each unit of delivered work is counted per customer, on success. The number never inflates.
Each connector, mapping, and check is versioned and reusable. The next customer of the same kind is configuration, not engineering.
Pipelines move data. Oliver understands it, reasoning the mapping, encoding the judgment, proving the result, and getting better with every run.
The first run needs your guidance. The tenth, on data like yours, is nearly autonomous. The hundredth is.
A new tenant of the same kind runs the same skills the platform already learned. Adding them is configuration, not engineering.