Timbal AI

Timbal AI is built around a specific thesis: that most enterprise AI initiatives stall not on model quality but on the gap between a demo and something the business can actually run on. The company addresses that by combining forward-deployed engineers — people who sit with the customer, learn a painful process and build the first working solution — with a single platform on which those solutions are then hosted, managed and extended by internal teams.

The platform spans three broad layers. At the build layer, teams construct agents, workflows and full applications, choosing models rather than being locked to one, and connecting to the systems where the real data lives. At the run layer, those applications serve end users across channels including voice, with retrieval grounded in company knowledge and business systems. At the governance layer, Timbal provides evaluation, monitoring, observability and audit trails, which is what makes the stack viable in regulated settings such as financial services and healthcare — the site emphasises that every run is replayable and that data can remain where regulation requires.

Deployment is deliberately flexible. Timbal describes offering custom domains, multi-seat collaboration with role-based access, SSO and SCIM, and both private-cloud and on-premise options, alongside a public AWS Marketplace listing. The claimed results are operational rather than abstract: fewer stockouts, faster document and lead processing, and lower order-error rates. Commercially, pricing is described as usage-based at the platform level with enterprise agreements for deployment and support, so the practical next step is a scoping conversation rather than a self-serve signup. Timbal is headquartered in Barcelona.