Service : build
Your business, with a perfect memory.
RAG (retrieval-augmented generation) connects language models to your own documents, policies and data, so answers come from your knowledge with citations instead of a model's memory. Novistu builds enterprise knowledge systems: ingestion pipelines, retrieval design, access control and evaluation that make answers trustworthy.
The mechanism
Knowledge systems that answer from your documents, policies and data, with citations you can check.
- Ask
- Retrieve
- Rerank
- Ground
- Cite
The problem
What this fixes
Knowledge exists but is unusable
Contracts in one drive, policies in another, expertise in people's heads. Everyone reinvents answers, and new hires take months to get up to speed.
Generic chatbots do not know your business
A raw language model has never read your product specs or compliance rules. Without retrieval it guesses, and confident guesses about your business are dangerous.
Search returns documents, not answers
Keyword search finds files; people need decisions. Getting from ten PDFs to one reliable answer is the work a knowledge system should do.
How we build it
Principles before code.
01
Model the knowledge
We audit what knowledge exists, who owns it, how fresh it is and who may see it. Access control is designed in from the start, not bolted on.
02
Engineer retrieval properly
Chunking strategy, embedding choice, hybrid keyword-plus-semantic search, reranking and metadata filters. Retrieval quality is where these systems are won or lost.
03
Ground every answer
Responses cite their sources, say when evidence is thin, and never blend documents silently. Users can check every claim in one click.
04
Evaluate continuously
Question sets built from real usage measure answer quality over time. When documents change, the system re-indexes and the evaluation suite catches regressions.
Delivery
From first call to running system.
- 01
Audit
Inventory sources, quality and permissions. Define the priority questions the system must answer and the quality bar.
1 to 2 weeks - 02
Build the pipeline
Ingestion, cleaning, chunking and indexing. Test retrieval against the priority questions before any interface work.
2 to 3 weeks - 03
Interface and guardrails
The answer experience: citations, follow-ups, feedback capture and access rules enforced end to end.
2 to 3 weeks - 04
Run and measure
Launch to a pilot group, evaluate weekly, expand sources and users as quality holds.
Ongoing
What you get
- Ingestion pipeline for your documents, wikis, tickets and drives
- Retrieval stack: chunking, embeddings, hybrid search, reranking
- Answer interface with citations and source previews
- Permission-aware access control mapped to your organisation
- Evaluation suite and freshness monitoring
- Deployment on your cloud or on-premise where required
Proof
Systems like this, in production.
Questions
Asked before every build.
Retrieval-augmented generation means the model looks up relevant passages from your documents before answering, then answers using those passages and cites them. It is the difference between an intern who has read your files and one who is guessing from general knowledge.
Uploading files to a chat gives you no access control, no freshness, no evaluation and no integration. A knowledge system enforces who can see what, stays current as documents change, measures answer quality and connects into the tools where people work.
Yes. We regularly deploy with open-weight models on client cloud or on-premise, with vector stores and logs on your infrastructure. Nothing leaves your environment unless you choose a hosted model deliberately.
Retrieval and embedding costs are modest; generation costs depend on usage and model choice. We size this during scoping, and cost controls per user and per query are part of the build.
The system surfaces the tension with citations rather than averaging it away. For high-stakes domains we add source precedence rules: newer overrides older, policy overrides guidance, and conflicts are flagged for review.
Tell us what is eating your team's hours.
A short brief, answered within one working day. The first call is free, and if AI is not the right answer, we will say so on that call.
First call free : honest about fit : no obligation