Internal knowledge assistants
Help teams find procedures and policies while respecting the permissions on the underlying material.
What you getPermission-aware retrieval and inspectable source references.
RAG & knowledge search development
Make your knowledge easier to find. And easier to verify.
Retrieval-augmented generation connects AI answers to the documents your team trusts. Build search and assistance around source quality, access permissions and answers people can check.
In the toolkit
- Embeddings
- PostgreSQL + pgvector
- Keyword search
- Language models
Choose with context
The right tool starts
with the right problem.
Where RAG & knowledge search fits
Useful when people need answers from changing policies, product documentation or internal knowledge that a model cannot reliably know on its own.
Worth considering
Retrieval cannot repair missing or contradictory source material. Citation links help users verify answers; they do not automatically make an answer correct.
What we can build
Useful answers begin with useful source material.
Knowledge assistantIllustrative workflow
An answer with somewhere to look
What evidence is needed for a damaged delivery?
- 1Find
Search permitted documents
- 2Check
Use the current policy version
- 3Cite
Link the supporting passage
Answer linked to the relevant policy
Customer support knowledge
Search approved product documentation and help articles, with clear escalation when a question needs account-specific investigation.
What you getA curated knowledge set, feedback loop and unanswered-question review.
Document discovery
Combine exact keyword matching and semantic similarity where each contributes to relevant results.
What you getA retrieval benchmark with representative questions and expected sources.
An example architecture
Find the evidence before composing the answer.
Evaluate retrieval separately from answer quality. Test document updates and deletions as carefully as the first import, and retain source identifiers throughout the pipeline.
- 01
Ingest & version
- 02
Filter by access
- 03
Retrieve & rank
- 04
Answer with sources
Adapted to your systems, data and operating requirements during discovery.
From first scope to handover
Agree on what “ready” means.
We define the first useful release and its acceptance criteria together. Working software, deployment instructions and the decisions behind it stay with your team.
- Restricted documents never reach unauthorized users
- Answers point to supporting passages
- Removed or superseded sources stop appearing
Do we need a separate vector database?
Not always. PostgreSQL with pgvector can be a practical starting point when your operational constraints and retrieval needs fit it. Larger or specialized workloads may justify a dedicated system.
Does RAG replace fine-tuning?
They address different needs. Retrieval supplies relevant source material at request time. Fine-tuning changes model behavior. We first identify whether the failure comes from missing knowledge, retrieval or the task itself.
Let’s work out what
your product needs next.
A new build, an existing codebase or one stubborn problem. Bring the context you have.
A little closer, wherever you are
Big world.
Close partnership.
Product engineering, AI and Odoo ERP, with a shared plan and a working rhythm built around your team.
Offices in India, Spain & France.
Different time zones. One shared conversation.
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