Assistant on your documentation
People ask in plain language and the assistant answers from your documentation — with sources cited, so it's easy to verify.
RAG · AI
RAG for business — AI that answers from your data instead of making things up
We build RAG (Retrieval Augmented Generation) systems that search your documents and knowledge base first, then answer — with sources cited. Instead of guessing, the AI reaches for your company's real data.
Free, no strings attached. At the end you get a PDF report breaking down your project.
The problem
A language model doesn't know your company's data and can make things up
An off-the-shelf AI model has no idea what's in your procedures, contracts and knowledge base. Asked for specifics, it sometimes answers confidently but incorrectly — and there's no way to check where it got it. In a company you need answers grounded in real documents, with the option to look at the source.
- AI doesn't know your internal documents and procedures
- answers can be made up yet sound credible
- you can't tell which source an answer is based on
- knowledge scattered across files, emails and systems
Who it's for
The biggest gains go to teams with a large knowledge base
If you answer the same questions over and over, or hunt for information across hundreds of documents, RAG can likely cut that work down to a few seconds.
- Companies with a large knowledge base and documentation
- Support and customer service
- Legal and compliance teams
- Product teams
- Agencies and advisory
- Technical teams
Example use cases
What we most often build on RAG
Semantic search
Instead of matching exact words, the system understands the meaning of a query and finds the right passages across the whole knowledge base.
Q&A over procedures
The system answers questions about policies, procedures and regulations — always based on the current version of the document.
Support team assistance
Agents get a ready draft answer grounded in the knowledge base right away, instead of digging through articles by hand.
Contract and document analysis
AI searches contracts and long documents, pulls out the key clauses and points to exactly where they sit.
Chatbot on your company data
A chatbot for customers or your team answers solely from your content, not from the model's general knowledge.
How implementation works
From documents to answers with a source
01
We collect and prepare the data
We connect to your documents and systems, tidy up the content and decide what should go into the knowledge base.
02
Chunking, embeddings and vector store
We split documents into meaningful chunks, turn them into embeddings and store them in a vector database for fast retrieval.
03
Retrieval + generation pipeline
We build a flow that first finds the right passages, then generates an answer with sources cited.
04
Evaluation, rollout and data updates
We check answer quality, deploy the system and make sure the data is updated on a regular basis.
Technologies
The tools we build this on
- OpenAI API
- Claude / Anthropic
- pgvector
- Embeddings
- Python
- PostgreSQL
- RAG pipelines
- LangChain / custom
- AWS
Risks and limitations
We're upfront about what RAG quality depends on
Input data quality
Outdated, messy documents lead to weak answers. We prepare and tidy up the content first, and only then build the knowledge base.
Retrieval relevance
If retrieval pulls the wrong passages, the answer will be wrong. That's why we evaluate and tune retrieval on real questions.
Data freshness
Knowledge changes. We build a regular update pipeline so the system answers from the latest documents.
FAQ
Common questions about RAG for business
What is RAG?
RAG (Retrieval Augmented Generation) is an approach where a language model first searches through your documents and knowledge base, and only then builds an answer based on what it found. This way AI responds using your company's real data, not just what it "remembers" from training.
How is RAG different from fine-tuning?
Fine-tuning changes the model's behavior itself and requires retraining every time the knowledge changes. RAG leaves the model untouched — it adds a search layer over your data, so you only need to update the documents to keep answers current. In practice RAG is cheaper, faster to deploy and easier to maintain.
Do answers cite their sources?
Yes. We design the system so every answer includes citations and links to the documents it was built from. That way you can verify where a piece of information came from, and users don't have to take the AI at its word.
Is company data secure?
Yes. We process data in the infrastructure you choose, with access control and without training models on your content. We match the setup to your requirements — from cloud to a deployment closer to your own systems — so documents stay within the agreed scope.
Next step
See how long your RAG project will take
Describe your case in a few steps and get a preliminary breakdown into modules, effort and risks. Then we can go through the report together.