AI development and chatbots · delivered in 14 days
This is an AI assistant built to answer only from the material you give it. Every response is traceable to a specific passage, and when the documents offer no answer, the agent tells you so instead of fabricating one.
Most chatbots sound certain even when they are wrong. Faced with a question the source material never addresses, they produce a plausible-sounding answer anyway - a risky habit when the underlying material is a contract, a policy or a support procedure.
We retrieve first and generate second. Your files are split into chunks that preserve structure, so headings stay linked to their paragraphs and tables are not broken apart into nonsense. Each chunk keeps a record of its source, page and section. Search combines dense embeddings, for conceptual matches, with keyword search, for exact identifiers such as part numbers, clause numbers and names that embeddings often miss.
The agent is told to answer strictly from what it retrieves and to reference those passages. If nothing relevant clears a confidence threshold, it says it cannot answer and can hand the question off instead. This behaviour can be adjusted, but it is the default because it is the one that avoids costly mistakes.
A sample set of your documents, along with real questions your users ask and the answers you would expect. These become the test set that confirms the agent performs before it goes live.
The agent can run on your own infrastructure or in the cloud, and the underlying model can be swapped. If your documents cannot leave your network, we design around a locally hosted model instead.
This is delivered as a single fixed-price build with one round of revision, typically within 14 days. Ingestion and retrieval are usually functioning against your documents early on, with the test questions showing where accuracy stands before any user sees the agent. We report a measured score rather than an impression, and every retrieval change is checked against the same test set so improvements and regressions are both visible. If your documents turn out messier than expected, you will know in the first days rather than at the end. You will need to supply the domain this order applies to and your project brief.
Your order opens its own thread here the moment it is paid, and everything about that order - questions, changes and the final report - happens in it.
It is possible, so we design against it: every answer is grounded in retrieved passages and referenced, and when nothing relevant turns up, the agent declines rather than inventing a response.
We choose whichever model suits your constraints - a hosted model where quality matters most, a local one where your data must stay in-house. The retrieval layer is what makes either option work reliably.
We build a test set of real questions with known correct answers, and run it before and after every change. That turns accuracy into a figure you can track rather than a guess.
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