Your documents know the answer. Now they can say it.
Policies, manuals, contracts, past proposals, that 400-page technical archive — your organisation's knowledge is written down but unfindable. We build chatbots over your own documents that answer in plain English and cite their sources, so people trust what they're told.
What teams use them for
Internal knowledge assistants
"What's our parental leave policy?" answered instantly with the paragraph cited — instead of a search through the intranet and an email to HR.
Technical documentation Q&A
Engineers and support staff querying manuals, specs and past incident reports — the ten-year veteran's recall, available to the new starter.
Customer-facing helpers
Product docs and FAQs made conversational on your website, with guardrails: sources shown, "I don't know" said honestly, and handoff to a human when it matters.
Contract & tender review aids
Ask questions across a folder of contracts or a tender pack: obligations, deadlines, deviations from your standard terms — with clause references for verification.
Onboarding assistants
New hires asking the questions they'd hesitate to ask a busy colleague — answered from your real processes, consistently.
Wherever you work
In your intranet, your website, Slack or Teams — built into the tools people already have open, which is where assistants actually get used.
Grounded answers or no answers
A chatbot that confidently invents policy is worse than no chatbot. We build retrieval-grounded systems: answers come from your documents, citations are shown, and "that's not in the documentation" is a first-class response. We test against a question set you help define before anything faces users — and we're upfront that these systems assist judgement rather than replace it.
What building one actually involves
The visible part — the chat box — is the last 10%. The work that determines whether a document chatbot is trustworthy happens earlier. Ingestion comes first: your documents live in SharePoint folders, network drives, email attachments and a wiki nobody has gardened since 2021, in formats from clean Word files to scanned PDFs. We build pipelines that extract text faithfully (tables and headers included — naive extraction mangles both), preserve the metadata that matters (which policy version, effective when, owned by whom), and re-sync automatically when documents change, because a chatbot answering from last year's policy is worse than none.
Chunking and retrieval come next, and they're where most DIY builds quietly fail. Documents must be split into passages that keep their meaning — a clause separated from its exceptions gives dangerously confident half-answers — and retrieval has to find the right passages, not just similar-sounding ones. We tune this against your real question set and measure it: retrieval accuracy is a number, not a vibe. Permissions are enforced at this layer too — the index respects your existing access controls, so the chatbot can't tell a junior what only HR should see.
Then evaluation before launch: we build a test set of questions with known-correct answers (you supply the awkward ones), score the system against it, and publish the results to you — including the failure cases. That's the difference between "we built a chatbot" and "we built a chatbot we can defend in front of your board."
Common questions
Where does our data go?
That's the first architecture conversation. Options range from UK/EU-hosted APIs with no-training agreements to fully self-hosted models for sensitive documents. We map your data-protection requirements before choosing anything — and document the choice for your DPO.
Our documents are a mess. Does that matter?
Less than you'd fear — wrangling messy PDFs, scans and SharePoint sprawl into usable form is a standard part of the build, and the pilot tells us early how much cleaning pays off.
What does it cost?
Fixed-price pilots typically run £5,000–£15,000: your real documents, a defined question set, honest accuracy measurement. Production rollout is quoted from the pilot's evidence — including running costs, projected so there are no surprises.
How many documents can it handle?
From a few dozen policies to hundreds of thousands of pages — the architecture scales, the ingestion effort is what varies. Volume matters less than variety: fifty clean PDFs are easier than fifty formats. The pilot establishes both.
How does it stay up to date when documents change?
The ingestion pipeline re-syncs on a schedule or on change events from your document store, re-indexing only what moved. Version metadata means the assistant can also say when a policy changed — often the question behind the question.
Can it answer in other languages?
Yes — modern models handle multilingual question-answering well, including answering in one language from documents written in another. If your organisation works multilingually, we include it in the pilot's test set so accuracy is measured, not assumed.
Which documents get asked about most?
Tell us what people keep searching for and where it lives. A developer will scope a pilot honestly — including whether you need one at all.
Discuss a document chatbot