AI document search for companies and institutions

What AI document search is

Classic search works on keywords: it finds documents containing exactly what you typed. That works when you know the file name or wording, but not when you ask “what is the notice period for the office lease” and the contract says “either party may terminate with 60 days’ prior written notice”.

AI document search understands the meaning of both the question and the text. Instead of a list of files, the employee gets a direct answer in a sentence or two, with the document name, clause, page and quoted passage. The answer can be used immediately, and the original opens in one click when a closer look is needed.

For the company this means less time spent searching, faster onboarding of new hires and less dependence on the few colleagues who “know where everything is”.

How RAG with citations works

RAG (Retrieval-Augmented Generation) is an architecture where the AI first finds the relevant parts of your documents and only then writes an answer based strictly on them. The model doesn’t answer from memory, but from passages it can cite.

In our systems it works like this: documents are processed and split into meaningful chunks, each chunk gets a vector representation from our embedding model, and search combines similarity by meaning with classic keyword matching. Reranking then selects the best few passages, and a fine-tuned language model writes the answer and cites its sources.

• Hybrid search finds exact names, numbers and codes as well as questions phrased differently
• Reranking reduces noise and gives the model only the most relevant passages
• Citation rules require the model to back every statement with a document and page
• When the documents contain no answer, the system says so instead of guessing

Why private fine-tuned models instead of public AI tools

Public AI tools are built for general conversation. They don’t know your contracts, don’t respect your access rights, and every document an employee pastes into them leaves the organisation. For legal, financial and medical data that is usually unacceptable.

ProCode therefore uses its own models, fine-tuned to the client’s domain: legal terminology, technical abbreviations, internal product names and the language the team works in. The models run on our private EU infrastructure or on your hardware, with no data sent to third parties.

A further benefit is predictable cost. There is no per-query billing from a foreign vendor, and the model never changes without your knowledge, so answer quality stays stable and measurable.

Preparing documents: scans, tables and versions

Answer quality depends most on how well documents are processed. That is why we pay special attention to this phase — and it is work the client doesn’t have to do.

Scanned documents are converted to text with OCR, tables are kept as tables, and annexes are linked to the main contract. Every chunk carries metadata such as date, document type, department and version, so the system can prefer the policy in force over an old draft.

• PDF, Word, Excel, PowerPoint and emails with attachments
• Scanned contracts, decisions and minutes via OCR
• Shared network drives, DMS platforms and SharePoint
• Automatic re-indexing of new and changed documents

How we measure accuracy and keep quality high

AI search is only useful if people trust it. That is why we don’t judge accuracy by eye, but on a set of real questions with expected answers and sources. For each question we check whether the system found the right document, whether the answer is correct, and whether the citation actually supports what the answer says.

After rollout, quality is monitored continuously. Users rate answers with one click, and unanswered questions reveal which documents need to be added or updated. We use this data to periodically re-tune the models and retrieval, so the system becomes more precise over time rather than drifting.

• A test question set prepared together with your subject-matter experts
• Retrieval accuracy and answer accuracy measured separately
• User ratings and a report on unanswered questions
• Regression testing before every new model version

Integrations with DMS, email and business applications

The best results come when AI search lives where people already work. Besides a standalone web interface, we embed search into your intranet, DMS, CRM or case management application, and expose it through an API for your internal systems.

Documents are pulled from shared network drives, SharePoint, DMS platforms and email archives, with automatic change tracking. When a policy is updated or a contract gets an annex, the new content is searchable without manual work, while the old version stays in the archive, clearly labelled.

Security, access rights and GDPR

AI search must never become a shortcut to documents an employee couldn’t otherwise see. We therefore import access rights from the system where the documents already live and enforce them before every search, not just when the answer is displayed.

The system logs queries and answers, supports single sign-on (SSO) and can run fully disconnected from the internet. Personal data processing complies with GDPR: data stays in the EU or on your premises, and access and retention periods are defined together with your data protection officer.

How to start: from pilot to the whole company

The safest path is a pilot on one document collection the team uses every day, such as contracts, internal policies or technical documentation. At the start we write around fifty real questions with expected answers together and measure accuracy on them before and after tuning.

Once the pilot proves its value, the system is extended to other departments and sources, connected to your DMS and SSO, and complemented with agents for summaries and document comparison. Investment only grows once the benefit has been confirmed on your own data.