Live Demo · AI Document Search

Ask your documents a question

This is a working demo of the kind of AI feature we build for clients. Below is a fictional company policy set. Ask it anything in plain English. No search terms, no folder-digging.


Try it

Northfield Digital Ltd, a fictional HR policy set. Five documents, plain-English search.

What's loaded: Expenses Policy · Remote Working Policy · Annual Leave Policy · Sickness Absence Policy · IT Equipment Policy. These are fictional documents created for this demo.

Try asking

Answer

Your question is sent to OpenAI to generate the answer. It is not stored or logged. The documents are fictional.


What you just used

The same pattern, built into your existing software.

01
Your documents, not ours

In a real engagement, the documents are yours: policy manuals, product specifications, knowledge bases, PDF archives or support material. The source formats, access permissions and update process are checked during scoping.

02
Built into your existing app

This isn't a standalone tool. It's a feature added to whatever you already have. A button in your internal portal, a tab in your CRM, a widget in your support desk.

03
Designed for production use

A real build adds error handling, rate limits, cost controls, source access rules, testing and a fallback when the documents do not contain an answer.


How a real project works

Same process every time.

1
First reply (30 min)

You describe the problem: what documents, who uses them, what outcome you want. I tell you what I'd build and what it costs.

2
Written scope

Fixed price, fixed scope, fixed timeline. No ambiguity before we start.

3
7-day sprint

I build it into your existing codebase or system. Short async check-ins. You stay focused on your work.

4
Handover

Working feature, walkthrough, documentation. One round of revisions included.


Document and data AI running in live products

The same retrieval and analysis work, already shipped and in use.

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Job Space AI

Built and shipped a production AI platform with CV analysis, document processing, AI-assisted job matching, and career tools. A commercial SaaS with paying users, built entirely by me.

Next.js · TypeScript · LLM APIs · Supabase

Case study ↗ · jobspaceai.com ↗
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Primary Story

AI reading platform for UK primary school children, with personalised story generation, comprehension tools, progress tracking, and teacher dashboards. Built and launched solo, end to end.

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Questions about document search

AI document search FAQ

What documents can be searched?

Common sources include policies, manuals, product documentation, support articles and text extracted from PDFs. Suitability depends on document quality, permissions, volume and how often the source material changes.

Does the assistant search the public internet?

This demonstration answers from its fictional policy set. A production assistant can be restricted to approved internal sources and should say when those sources do not contain the answer.

Can answers show their sources?

Yes. A production version can return document names, sections or quoted evidence so staff can verify an answer against the original material.

How are confidential documents protected?

Access should follow the organisation’s existing permissions, with suitable authentication, logging, retention and provider settings. Different teams should not automatically see the same source set.

What happens when a policy changes?

The index needs an update process so amended or withdrawn documents replace stale material. The right approach depends on where the source documents are managed.

Have a document problem worth solving?

Tell me what you have and what you want staff to be able to do with it. I'll tell you honestly whether it's a fit and what it would cost.

Send your brief · reply in 24 hours nicholas@nrbconsulting.co.uk