LLM & RAG Integration
Relayworks AI builds retrieval-augmented systems over your company's own knowledge — documents, tickets, wikis, product data, policies. We handle chunking, embedding, retrieval, reranking, grounding and citation, and we measure answer quality against a labelled set rather than asking you to take it on faith.
What this solves
Most RAG projects fail at retrieval, not generation. If the right passage never reaches the model, no amount of prompt engineering saves the answer — and the failure is invisible, because the model produces something fluent and wrong.
What is included
- Corpus preparation
- Parsing, cleaning, chunking and enriching your documents. Unglamorous, and the largest single determinant of quality.
- Retrieval design
- Hybrid semantic and keyword search, metadata filtering and reranking — tuned against your actual queries.
- Grounding & citation
- Answers that cite the source passage, so a human can verify in one click.
- Refusal behaviour
- The system says "I do not know" when the corpus does not contain the answer. This is the single highest-value behaviour in an internal knowledge tool.
- Evaluation
- A labelled question set with retrieval and answer metrics, run on every change to the corpus, the model or the prompt.
- Freshness pipelines
- Incremental re-indexing as documents change, so answers do not quietly go stale.
What working with us looks like
| Detail | |
|---|---|
| Typical duration | Six to twelve weeks |
| Commercials | Fixed scope and a fixed number, agreed before work starts. No hourly billing. |
| Team | Senior-led. The people who scope the work do the work. |
| Code ownership | Yours outright, including the evaluation suite and infrastructure definitions |
| Evaluation | A labelled test set from your real cases, with thresholds agreed before build |
| After launch | Defect warranty, then optional managed operations |
LLM & RAG Integration
No. We use commercial API tiers with training disabled, or self-hosted open-weight models on your infrastructure where policy demands it. This is written into our contracts and covered on our security page.
You reduce it; you do not eliminate it, and anyone promising elimination is overselling. We constrain answers to retrieved context, require citations, measure groundedness on a labelled set, and design explicit refusal behaviour. The residual risk is then known and monitored rather than assumed away.
Retrieval systems work at surprisingly small scale — a few hundred good documents often outperform tens of thousands of poor ones. Corpus quality matters far more than corpus size.
Other services
AI Consulting & Strategy
Find the two or three processes where AI actually pays, model the return, and prove it with a working prototype — in two weeks.
AI agent developmentAI Agent Development
Agents that take real actions in real systems — with tool access, guardrails, escalation paths and an evaluation suite that proves they work.
Process automationAI Process Automation
Automating the document-heavy, repetitive, judgement-light work that consumes your team — with AI where it earns its place and plain code where it does not.
Managed AIManaged AI Operations
Someone accountable for your AI system after launch — monitoring, evaluation, model migrations, cost control and a monthly report against the metric.
Let’s find out what AI can actually do in your business.
A 30-minute call. We will tell you honestly whether there is a case worth building — and if there is not, we will say so.