AI Document Assistant for Company Documents

Who a document assistant is for

In document-heavy companies the answer usually exists – nobody remembers which file it is in. A lawyer hunts for a similar clause across hundreds of contracts, a service engineer flips through manuals for a dozen equipment models, the bid team checks whether a requirement has been met before. Keyword search fails as soon as the question uses different words from the document.

The assistant combines semantic search with a language model: it understands the question, finds the relevant passages and composes an answer with citations. Typical users include:

• legal and compliance teams – contracts, terms, policies;
• service and technical support – manuals, service bulletins, inspection reports;
• sales and bid teams – tender documents, past proposals, project references;
• HR and administration – internal regulations, procedures, directives;
• consulting and engineering firms – reports, expert opinions, meeting notes.

What the assistant can do

Hybrid search

Meaning-based search combined with classic word and number matching – essential for reference numbers, part codes or clause numbers that semantic models tend to blur.

Answers with citations

Every statement in an answer is tied to a source passage. One click opens the document on the right page with the paragraph highlighted, so checking takes seconds.

Summaries and comparisons

A digest of a long report, a list of differences between two contract versions or a side-by-side table of terms from several suppliers.

Extraction into tables

Pulling the same fields from a series of documents – parties, term, notice period, penalties – and exporting them to a spreadsheet or another system.

Checklist review

Checking a new document against an internal checklist, for example whether a contract includes confidentiality and limitation of liability clauses, and flagging what is missing.

User feedback

Staff mark helpful and unhelpful answers and report wrong sources; their notes land in the content owner's panel.

Document sources and access control

The assistant connects to where documents already live: SharePoint and OneDrive, Google Drive, Confluence, a document management system, network drives or ticket attachments. It handles PDF, DOCX, XLSX, PPTX, HTML and email. Scans without a text layer first need OCR recognition, which can be part of the same pipeline.

The key principle: source permissions carry over into search. If an employee cannot open the board's folder, the assistant will not use a single sentence from it. Permission lists are synchronised with your identity provider (e.g. Microsoft Entra ID, Google Workspace, SSO via SAML or OpenID Connect), and a document deleted at the source disappears from the index at the next sync.

Architecture and deployment options

The indexing pipeline reads each document while preserving its structure (headings, tables, clause numbering), splits it into passages, adds metadata – author, date, type, project – and stores it in a hybrid index such as OpenSearch, Elasticsearch or PostgreSQL with pgvector. At query time a reranking model reorders the results before they reach the language model.

The deployment option follows data sensitivity:

• your cloud tenant in an EU region (Azure, AWS, Google Cloud) with an API model in the same region;
• your own infrastructure with an open-weight model – when documents may not be sent to an external provider;
• a hybrid setup: index and files stay on-premises, and only the passages needed for a given answer go to the API.

The interface can be a web application (React or Next.js), a Microsoft Teams app or an API called from an existing system.

How answer quality is measured

Together with experts on your side we prepare several dozen questions with answers and the document where each answer sits. We measure two things separately: whether search found the right passage, and whether the model used it correctly. That shows whether to improve indexing or the model's instructions. The set also includes questions the documents cannot answer – a good assistant should then say it found nothing rather than guess.

Permission tests form a separate group: test accounts with different roles ask the same questions, and the results must match the access matrix.

Confidentiality, GDPR and limits of responsibility

Company documents contain personal data, trade secrets and information covered by confidentiality clauses. The specification defines where the index is stored, how long queries are retained, the access log and the model vendor's terms, including opting out of training on your data. In solutions serving several companies or clients, indexes are separated so that no query can cross an organisational boundary.

The assistant speeds up work but does not replace legal, financial or employment decisions. In high-impact use, an answer is a starting point for review by a specialist, and the interface says so explicitly.

Rollout step by step

We start with an inventory: which document collections exist, in what formats, how many files, who can access them and how often they change. From that we pick one collection and one team for the pilot. The specification covers sources, the permission model, first-version features, the test questions and acceptance thresholds. We then build the indexing pipeline, interface and identity provider integration in iterations that the pilot team reviews as they go. After acceptance the assistant expands to further collections, and maintenance covers quality, cost and index freshness.

Cost and what influences it

A pilot on one document collection with a single source and a simple permission model starts from $2,900. The main budget drivers are:

• the number and variety of sources, each needing a connector;
• permission complexity – a shared folder is very different from file-level permissions;
• the share of scans and complex layouts such as tables or technical drawings;
• features beyond search: extraction, comparisons, checklists;
• the deployment option – your own infrastructure with an open-weight model takes more work than an API.

Model usage, vector storage and cloud hosting are outside the project price. Pricing principles are on the pricing page, and other solution types in the AI software development category.

What the client prepares

• a representative pilot document set with test access;
• questions from the team's real work and the expected answers;
• a description of roles and permissions, plus identity provider access;
• a person who will assess the pilot and decide on expansion.

If the goal is rather to answer customers on your website, see AI chatbot development. You can describe your document collection and first questions via the contact page.

Related services

Frequently asked questions