AI for internal documentation and corporate knowledge
Ideasforge builds artificial intelligence systems connected to companies’ internal documentation and knowledge: assistants that can look up manuals, procedures, regulations, technical documentation and knowledge bases in natural language.
The goal isn’t to upload documents to a chatbot. It’s to build a layer of access to knowledge that controls which information is retrieved, who can look it up and where each answer comes from.
Knowledge nobody can find costs time and money
The problem is a common one. Someone needs a technical detail, a procedure or the history of a problem, and the answer exists but it’s buried. They ask a colleague, the colleague asks the company’s longest-serving person, and that person knows it by heart because the document that explains it runs to two hundred pages.
The cost isn’t only the lost minutes. It’s the dependence on a few people, which turns every sick leave and every retirement into a risk for operations.
- Shared folders
- SharePoint
- Google Drive
- PDFs
- Word documents
- Wikis
- Manuals
- Procedures
- Emails
- Knowledge bases
- Internal applications
- Technical documentation
What can AI do with a company’s documentation?
A search engine returns ten documents where the answer might be, and the person still has to read them. This assistant answers the question and adds the reference to where the answer came from.
Answer questions
“What’s the procedure for approving a purchase over €10,000?” The system looks for the information and builds an answer based on the internal sources available.
Find relevant information
Locate the right passage in large amounts of documentation even when the question doesn’t use the same words as the document.
Query live data
Some questions no document answers well, because the answer changes every hour. For those, the assistant queries the internal system that holds the data and answers with what’s there right now.
Summarize documentation
Produce summaries of documents, procedures or specific sets of information.
Guide processes
Explain which steps an employee has to follow according to internal documentation, or walk them step by step through a diagnosis.
Cite the sources
Include references to the documents used so the user can check the original information.
Question → search → retrieval → answer → sources.
We don’t train a model on your documents. When the user asks, the system first looks for the relevant information in the authorized sources and then gives it to the model so it answers from your sources, not from its general knowledge. This approach is known as RAG.
A demo RAG can handle the easy questions. A production one has to find relevant information even in dense documents, with in-house vocabulary and badly phrased questions.
We connect AI to the sources where knowledge already lives
It isn’t always necessary to move documentation to a new platform. The architecture decides whether the information has to be copied, indexed, synced or queried directly.
Documents
PDF, Word and other document formats.
Knowledge bases
Wikis, structured documentation or internal platforms.
SharePoint and corporate storage
Retrieve and sync information stored in business environments.
Google Drive
When documentation is managed in the Google ecosystem.
Databases
When part of the knowledge is stored as structured information.
APIs
Retrieve up-to-date information from other applications.
In-house software
Integration with tools built specifically for the company.
AI applied to real technical knowledge
The plant assistant splits the work across specialized agents. That way new domains can be added without changing the agents that already work, each agent can be measured on its own, and a failure in one doesn’t drag down the rest.
How we build an AI system over corporate knowledge
Development starts by understanding what information the user needs to find.
01 We identify the questions
Which questions employees, customers or users ask today. That defines what kind of knowledge has to be reachable.
02 We analyze the sources
Where the information is, which formats exist, how good it is and how it gets updated. We identify duplicates, versions and outdated content.
03 We design the retrieval
How to find the right information for each query: semantic search, metadata, filters, databases and other mechanisms.
04 We define permissions and sources
Which information each user can look up and which sources the system can use to answer.
05 We evaluate answers
Real questions: whether it retrieves the right thing, whether the answer is backed by sources, whether it cites properly, whether it respects permissions and what happens when there isn’t enough information.
06 We measure in production
A battery of real cases before every change and a weekly batch of test conversations on the live system. Unanswered questions end up as tasks for whoever owns the documentation.
Each person can only look up what their permissions allow
The assistant uses the same permissions the person already has in the company’s systems, so nobody gets a passage in an answer that they couldn’t open by hand. When it queries an internal system, it does so with the person’s identity and permissions, not with a service account that has global access.
And when the answer isn’t in the sources, the assistant says so, plainly and with a trace. An assistant that fills gaps with false confidence is exactly what we don’t build.
The assistant’s permissions and controls
- 01 User
- 02 Role
- 03 Department
- 04 Source
- 05 Document
- 06 Type of information
- 07 Sources cited in every answer
- 08 Answers limited to the retrieved content
- 09 A warning when something isn’t documented
- 10 A log of unanswered questions
- 11 Test suite before every change
Not every knowledge problem needs RAG
RAG is a useful architecture, not an automatic fix for any documentation problem. We don’t implement RAG because it’s popular. We use it when it improves knowledge retrieval.
Types of knowledge where it adds value
- Technical documentation
- Internal procedures
- Regulations and compliance
- Product and service
- Internal support
- Training and onboarding
- Scattered operational knowledge
Depending on the case we combine
- Semantic search
- Keyword search
- Filters and metadata
- Hybrid retrieval
- Databases
- APIs
- Specific rules
- RAG
Frequently asked questions about AI and internal documentation
What is a RAG system?
An architecture that looks for relevant information before asking the model to generate an answer. Instead of relying on the model’s own knowledge, it retrieves information from specific sources and uses it as context.
What’s the difference between RAG and training a model?
RAG retrieves outside information when it answers. Training or fine-tuning a model changes its internal knowledge. For business documentation, RAG makes it easy to update the sources without retraining.
How much does a corporate knowledge assistant cost?
It’s quoted per project, because the cost depends on the volume and state of your sources, how many systems get integrated and how much validation you demand before going live. Multi-agent systems of this size sit outside the standard ranges we publish for single-job agents. The price structure, build plus monthly operation, is the same, and the cost guide breaks it down.
AI agent development cost, broken down → What about questions the documentation doesn’t cover?
The assistant says so, plainly and with a trace. We separate “outside the product” from “missing documentation”, each with its own answer and its own work queue, so real gaps end up as tasks for whoever owns the documentation. An assistant that fills gaps with false confidence is exactly what we don’t build.
Will employees see documents that aren’t theirs to see?
No. The search that feeds the assistant respects each role’s permissions, so a restricted document never even reaches what the model reads before answering. And system queries travel with the identity of the person asking, under their usual permissions. If someone can’t open a record by hand, their assistant can’t either.
How does it stay current when documentation changes?
Documents are processed again whenever they change, and quality is watched in two ways, a battery of real cases before every change we ship and a weekly batch of test conversations on the live system. When something stops being found or understood, it shows in the metrics before it becomes a complaint.
Does it handle our in-house vocabulary, and several languages?
Your in-house vocabulary is exactly the hard case retrieval gets calibrated for, with real cases from your people rather than lab examples. The languages you need are defined during exploration, which is the first phase of the project.
Can it run without leaving our infrastructure?
The full system runs on infrastructure you own, cloud or your own servers. The code is yours from day one, in your repository. The call to the language model is the only external piece, and it runs under the agreements and settings you approve, without your content being used for training.
Deploying the model itself on your machines is a different project, with other costs and a different quality trade-off. If it’s a requirement it needs to come up in the first conversation, because it changes the architecture from the ground up.
How long does it take?
Exploration, which is the first phase of the project, answers that with your sources in front of us, not before. The shape of the project is constant though, a first bounded knowledge domain that reaches production with its measurement in place, then growth domain by domain. The industrial case grew exactly that way.
Other solutions
Tell us where your company’s knowledge lives today
Tell us what information your team needs to find, where it’s stored and who should be able to look it up. We’ll look at how to make it reachable without losing control over sources, permissions, versions and security.