Most AI projects do not fail at the model. They fail in the gap between the data and the model. That is the part where a connector breaks, where nobody can say where a number came from, and where the vocabulary of the business does not survive the trip into a column name.

Gwenlake Atlas is the platform we built to close that gap on our own projects. It is now available as a product.

What it does

Atlas covers five steps that have to hold together, because a break in any one of them shows up as a wrong answer at the end.

Connect and read. Around twenty connector types (CRM, document stores, mailboxes, databases, object storage, git, any REST API), with credentials held in a dedicated secret store rather than in a notebook.

Transform. OCR, chunking, embedding, vectorisation and joins across sources, with provenance carried on every row down to the source file and page. If an answer cites something, you can open what it cited.

Ontology. Concepts and relations derived from your data, so models and agents read your vocabulary rather than column names. This is the step most pipelines skip, and the one that decides whether a retrieval system answers questions in the language the business actually asks them in.

Models and agents. The prepared material goes to the models we build for you, or to Gwenflow agentic pipelines. Schedule them, and watch every run.

Connect and write. Outputs pushed back into Salesforce, Dynamics 365, Power BI, Tableau or your own applications, through the same connections Atlas read from, or through your APIs. A result that stays in the platform is a result nobody uses.

Run it where your data lives

The same platform, three ways to run it. Pick the one your security team is comfortable with; moving between them later is a deployment change, not a migration.

	What it is
Fully Managed	Hosted and operated by us on sovereign European infrastructure.
On your cloud	Deployed in your own cloud account, your keys, your network.
On-premise	Installed on your own servers, air-gapped if required.

What it costs, as it happens

Atlas records every call with its model, its prompt and completion tokens and its cost, run by run rather than as a monthly total. It measures the energy each LLM call draws and its environmental impact per session, as it happens, instead of estimating it at the end of the year. Usage breaks down by user, by project and by application, and every agent run is traced through OpenTelemetry to any OTLP backend, with the lineage of the data it touched sitting alongside the trace.

None of that is a dashboard we added at the end. It is the part that makes the platform defensible in a review, which is why it was in the design from the start.

Read more about Atlas, or talk to us about what it would take to run it on your data.