Answers from your own documents, in seconds
A firm's documents are only worth what can be found in them. When retrieval is slow, people stop asking and start guessing, or they ask a colleague who is also guessing.
What it does
- 01Indexes the document estate you already have, wherever it sits
- 02Answers questions with the passage and the document behind the answer
- 03Keeps the index current as documents are added, without a rebuild
- 04Is designed so running costs do not climb with every document added, which is where these systems usually fail
What it changes
- Analysts stop giving up because finding things takes too long
- Institutional knowledge survives people leaving
- Running cost becomes predictable
What you can check
Every answer cites its source. The index can be rebuilt and the results compared, which is how the case study below was measured.
What it looks like
Illustrative. Built around your firm, your clients and the systems you already run.
Eight times faster, at a quarter of the running cost
A data and analytics provider in Dubai serving central banks and investment banks.
Average response time
Slowest question, time until the answer begins
Running cost
Measured on the client's own question set, before and after the rebuild.
The client’s platform answers research questions across a very large estate of documents and data, the kind of question that would otherwise take an analyst a day of reading. In a product like this, speed is what customers are paying for.
Its own customers were calling the product unusable. Answers took so long that people stopped asking questions, and from the outside there was no obvious reason why. Behind each answer sat a search across a very large corpus, a chain of tool calls running one after another, and far more data than any single question needed. The slowest questions in the client’s test set took more than two minutes.
- 01Redesigned the retrieval pipeline from the ground up, because the limits were built into the design and no amount of tuning would remove them.
- 02Parallelised the tool calls that had been running one after another, removing most of the waiting.
- 03Matched the model to each task, tested against a fixed set of questions, so the most expensive model is used only where it measurably improves the answer.
- 04Added caching wherever the system was repeating the same work for every question.
- 05Introduced continuous evaluation, so accuracy and speed are measured every time the system changes, not discovered through a customer complaint.
In systems like this, speed, accuracy and cost are decided by the design. When a product feels slow to the people paying for it, the fix usually lies in how the work is structured, not in how much computing power sits behind it.
The client was operating in stealth at the time, so we describe it by sector and location instead of by name.
Questions
- How is accuracy checked?
- Against a set of real questions with known answers, before and after, on your own documents.
- Does it need our documents moved?
- No. It reads them where they are.
- What happens as the estate grows?
- Cost per query is set by the design, so growth in the estate is planned for from the start.
Start with a conversation
The first conversation is free. We will talk through where your team’s time is going and tell you plainly whether we can help.
Last updated 14 October 2026