About

We kept meeting the same problem.

Across years of work with hedge funds, fintechs, healthcare providers and manufacturers, one thing recurred in every engagement regardless of the industry: these organizations hold an enormous amount of data and almost no way to act on it. Not because anything is missing, and not because nobody is capable. Because organizing that data into something answerable is expensive, and it has to be paid for again every time a new question is asked.

A fintech has every transaction it has ever processed. Every payment, every transfer, every reversal, timestamped and retained for years. Ask it which segments its revenue actually comes from and the answer takes weeks of work, because nothing in those records says what a segment is.

A plant has sensors along the length of the production line emitting a signal every second, and years of them stored. Ask it which asset to service before the next outage and there is no basis for an answer, because nothing connects a signal to the equipment it describes, or that equipment to the failure it is drifting towards.

In each case the missing piece is the same, and it is not the data, not the compute, and not the data warehouse built to consolidate them. It is a description: which entities exist, how they relate, and what the numbers actually mean. Every organization we worked with had built fragments of that description by hand, inside a report, a spreadsheet or in one analyst's memory. Then built them again for the next question, and the one after that.

So we built Contour to hold that description once and keep it. It aggregates the source systems you already run and enforces a semantics over them that is specific to your business, so a question is answered against a model rather than reconstructed from scratch. Your team queries it directly. So do your agents, over MCP.

It is also why we do not open with a demonstration. A model pointed at an undescribed warehouse will answer anything you ask it, and the answers it gets wrong are indistinguishable from the ones it gets right. The description has to exist first, and it has to be validated against records you already trust.

Figure 1The same question, sent both ways. Above, the assistant composes its own query against tables nobody ever described to it. Below, it calls operations defined over a model of your business, and the answer returns with the records it stood on.

We work with clients across Europe, North America and Brazil.