A reasoning platform for the databases you already have.
Tried pointing AI at your databases and got nowhere? Contour ingests your sources and builds the model they never had. You point an agent at it over MCP and ask in plain language.
You have a data warehouse, probably a data lake, and fresh data landing every minute. None of it records what any of it means. Which of the four revenue figures do people actually use? What counts as a segment here? Which table is the authoritative one? Your team knows. Nothing you own writes it down. So an assistant pointed at that data has to guess, and a good guess is indistinguishable from a right answer. We write it down once, in your vocabulary, and every number that comes back is one you can defend when someone asks where it came from.
IngestResolveModelAnswer≈ 3 months
Roughly three months from first access to answers you can put in front of somebody. This estimation takes a specialist modelling of your own domain and the integration of your source systems. We read from those systems; we do not write to them.
Where we work
Examples of what we can solve.
The platform is domain-neutral. These are examples rather than a catalogue, and the same structure applies wherever the records exist and nothing in them records what they mean.
Reliability & maintenance
A line instrumented end to end, where the interval on which any given asset is serviced still comes from the calendar rather than from that asset's own failure history.
Revenue & commercial data
Every transaction the business has processed, held and queryable, and four figures for the same quarter that never reconcile.
Hospitals & clinical engineering
A planned maintenance schedule that says what is due this month and nothing about which failure would close a theatre.
Figure 1Four source systems, one model. The solid line is the answer; the dashed line is that answer traced back to the records it came from.
Contour runs against whatever you already have. Modules go deeper where an answer has to understand a particular equipment or an internal process, not just tables.
Every module can provide customized scenarios for your business. We have here four examples that are commonly used expected:
Connect the assistants and agents your team already uses to your business, over MCP. They answer from verified operations rather than from a query they wrote themselves, and every figure carries lineage back to source.
See what a single failure takes down with it. Contour models the plant as a dependency graph rather than a list of tags, so consequence can be traced to the production it costs you.
Plan against the outage you are actually scoping. Each asset's own failure history is fitted with established reliability statistics and becomes a probability of failing inside the window you set, ranked by what that failure would take with it.
The layer the other two stand on. Contour ingests instrumentation continuously and resolves every tag to the equipment it measures, which is what lets a decade of time series be read against a decade of failures at all.