What does an internal AI assistant for your own documents cost?
Updated: 2026-10
The cost of an internal AI assistant consists of building and running it. Building depends on document volume and formats, permission model, integrations and quality requirements; running depends on model choice, hosting and usage volume. A fixed-scope prototype before expansion has proven itself.
An internal AI assistant that answers questions from your own documents and cites sources has several parts: document preparation, permission-aware search, language model, interface, evaluation and operations. Each part can be simple or demanding.
Unlike classic software, AI features incur ongoing costs per use, because every request to a language model consumes tokens. An honest cost view therefore separates build and operation.
What drives the cost
Document volume and formats
Well-structured text documents are prepared quickly. Scanned PDFs, tables and inconsistent formats need extra processing, such as text recognition and structuring.
Permission model
If all staff may see everything, search is simple. If content is separated by department, mandate or role, search must enforce those rights before content reaches the model.
Integrations
Connecting existing document stores, business systems or communication tools increases effort but is what makes the assistant useful day to day.
Quality requirements
An evaluation set of realistic questions is mandatory. The higher the accuracy requirements, for example with legal or specialist content, the more extensive evaluation and tuning become.
Model and data residency
The choice of model provider and hosting by data protection requirements affects both build and running costs.
Usage volume
The number of users and requests determines ongoing model costs. Quotas and caching keep them predictable.
How we price
- 01
Fixed-scope prototype
A limited document set is prepared and tested with real questions; the result shows whether and in what form expansion pays off.
- 02
Fixed-price expansion
Extension to the full scope with permissions, integrations and evaluation at a price agreed in advance.
- 03
Transparent running costs
Model usage and hosting are shown separately and measured so they are traceable and capped at all times.
We deliberately publish no online prices: every case differs. After a short first conversation you receive a binding fixed price or clear frame.
Parent service: Custom Software Engineering
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Internal AI assistant with source citations for your own documents
An internal AI assistant answers questions based on your own documents — manuals, policies, contracts, project files — and cites the source for every answer. What matters are permissions, so each person only sees what they may see, evaluation of answer quality and a deliberate choice of model and data residency.
AI integration strategy
An AI integration strategy establishes where artificial intelligence creates genuine value in your organisation — and where it does not. It identifies use cases, assesses feasibility and benefit, prioritises, and delivers a plan that builds in AI Act conformity from the start rather than retrofitting it.
FAQ
How high are the running costs?
They depend on the model, request volume and answer length. With per-user quotas and caching they stay predictable; measurement starts on day one.
Why a prototype first?
Because only real documents and questions show how good the answers will be and what effort the full scope needs. That protects against misinvestment.
Is an office-suite assistant not cheaper?
Often yes, if the documents live there and general tasks dominate. The decision guide on standard versus own assistant weighs both routes.