Baseline
Weeks 1 to 2
We connect a sample of your documents, collect real questions from your team and build a first version to measure.
- Document sample connected
- 50 to 200 real questions
- First accuracy numbers
Answers from your own documents, with a source for every claim.
Retrieval-augmented generation (RAG) lets a language model answer from your documents and data instead of from memory. We build the full pipeline, from ingestion to evaluation, so that answers are correct and can be checked.
What we build
The whole pipeline, not only a vector database.
Most weak RAG systems have a retrieval problem, not a model problem. We give most of the effort to finding the right passage, and we measure it.
Ingestion
Connectors for your PDFs, wikis, tickets, databases and file shares. Documents are parsed, cleaned and kept in sync when the source changes.
Chunking
Documents are split along their real structure, such as headings, tables and clauses, so that each piece keeps its meaning and its reference.
Hybrid search
Keyword search and vector search together, on pgvector and PostgreSQL. Exact terms such as product codes are found as well as paraphrases.
Re-ranking
A second model sorts the candidate passages and keeps the few that answer the question. This is often the largest single quality gain.
Answers with citations
The model answers only from the retrieved passages and links each claim to its source. If the documents do not contain the answer, it says so.
Evaluation
A set of real questions with known answers. It measures retrieval and answer accuracy before and after every change.
How a project runs
Measure first. Then improve.
We build the evaluation set before we tune anything. After that, each change to chunking, search or prompts shows as a number that goes up or down.
Improve
Weeks 3 to 6
We work on the weakest stage first: parsing, chunking, search or re-ranking. You can try each version.
- One change at a time
- Accuracy tracked for each change
- Weekly demo
Launch
After that
Access control, monitoring and a feedback button go in. The system goes to users and the evaluation keeps running.
- Per-user document access
- Monitoring and feedback loop
- EU hosting if you need it
FAQ
Questions about RAG.
What is RAG?
RAG means retrieval-augmented generation. When a user asks a question, the system first searches your documents for relevant passages. It then gives those passages to a language model, which writes the answer from them and cites its sources.
Can you build a RAG system on our own documents?
Yes. We build the whole pipeline: ingestion of your documents and data, chunking, hybrid keyword and vector search, re-ranking, answers with citations, and an evaluation set that measures accuracy before and after every change.
RAG or fine-tuning: which do we need?
For questions about your own content, RAG is almost always the first choice. It uses current documents, shows its sources and needs no model training. Fine-tuning is useful for tone, format or a narrow skill, and it can be added later.
How do you stop the system from making things up?
The model is told to answer only from the retrieved passages and to cite them. A check then compares each claim with its source. Questions the documents cannot answer get an honest "not found". The evaluation set measures how often each of these works.
Can each user see only the documents they are allowed to see?
Yes. Access rights from the source system are stored with each passage and applied in the search step, so a user never gets an answer built on a document they cannot open.
Can our data stay in the EU?
Yes. We can host vector stores and application servers in EU regions, choose model providers and settings that meet your data-processing requirements, and design for GDPR from the start.
Related
Next steps.
AI agents
When answering is not enough and the system must also act, retrieval becomes one tool of an agent.
RAG readiness review
A short, fixed-scope review of your documents, questions and risks before you commit to a build.
How we work
Short cycles, a working demo every week, and code and accounts that belong to you.