Financial research without the query language. Reckonsys built a platform where analysts ask questions in plain English and get answers assembled across historical, forecast and benchmarking datasets - and from the documents those datasets don't cover.
Financial data is only as accessible as the query language standing in front of it. Virtua Research holds datasets that answer real analyst questions, but reaching them meant knowing how the databases were structured and how to write against them. The people with the questions were not always the people who could write the query.
Reckonsys built a natural-language layer over that estate. An analyst asks in everyday language; the system works out which datasets the question touches, retrieves across them, pulls in supporting context from documents, and returns an answer. No SQL, no schema knowledge, no waiting on someone who has both.
A question like "how does this compare with peers" is not one query. It is several, against datasets that answer different kinds of question. Organising the estate into meaningful categories is what makes routing a natural-language question possible at all.
The hard part of natural-language querying in finance is rarely the language. It is that an analyst's question implies a set of datasets and a way of relating them, and none of that is stated in the question. Categorising the estate first is what turns an ambiguous request into a resolvable one.
This is the difference between a query tool and a research tool. Numbers tell an analyst what moved. Documents tell them what the company said about why. Answering both from one question is what removes the manual step of going to find the filing.
Claude sits at the point where an analyst's question meets a set of databases that know nothing about how analysts talk. It interprets what is being asked, determines which of the categorised datasets the question actually requires, and assembles the retrieved figures and document context into a single answer.
Financial language is why the interpretation step is harder than it looks. Margin, growth, peer group and comparable all carry precise meanings that shift with context, and a question phrased casually still implies a specific calculation over specific periods against a specific comparison set. Getting from that to the right retrieval is a reasoning problem, and it is the one Claude is doing.
The assembly step matters just as much. Combining a forecast figure, a peer median and a line of management commentary into something an analyst can act on requires holding several sources in view at once and being careful about what each one actually supports. That is where a capable model earns its place over a query translator.
Financial data exploration that no longer requires knowing how the data is stored.
Analysts reach historical, forecast and benchmarking data, and the documents around them, by asking. The technical barrier between having a question and getting an answer is gone, and research that previously meant several queries and a manual document search becomes a single request.
Let's collaborate to turn your business challenges into AI-powered success stories.
Get Started
