RAG implementation services

Knowledge assistants that answer from YOUR documents — with citations, permission-aware retrieval, and hallucination controls designed in.

Scope Your RAG Assistant

Plain English

What RAG actually is

Retrieval-Augmented Generation means the AI looks up your documents first, then answers using what it found — and shows you the source. Instead of relying on what a model memorised in training, it reasons over your private knowledge, securely and on demand.

The result: an assistant that answers employee and customer questions from your policies, manuals, contracts, and tickets — without leaking data to public models and without the hallucinations of a raw LLM.

Architecture

RAG vs fine-tuning — and how we build it

	RAG	Fine-tuning
Knowledge updates	Instant — update the document index	Requires retraining cycles
Citations	Native — every answer traces to a source	Not traceable
Permissions	Enforced at retrieval time	Baked into weights (risky)
Cost	Lower — no training runs	Higher — data prep + training

1. Ingest

Connect document sources: shares, wikis, ticketing, contracts — with incremental sync.

2. Chunk & embed

Split into meaningful passages and index embeddings tuned for your domain language.

3. Retrieve securely

Permission-aware retrieval: users only ever get answers from documents they're allowed to see.

4. Generate with citations

Answers grounded in retrieved passages, with sources shown inline.

5. Evaluate

A held-out question set scores accuracy before launch — and after every change.

6. Operate

Freshness syncs, drift monitoring, and cost tracking in production.

Use cases

Where RAG pays for itself fastest

Internal knowledge

Policies, SOPs, and engineering documentation — stop answering the same Slack question for the ninth time.

Customer support

Tier-1 answers grounded in your help centre — see support agents.

Contracts & compliance

Clause lookup and obligation summaries with document-level citations for audit.

Onboarding

New hires self-serve institutional knowledge from day one.

Field teams

Manuals and troubleshooting guides on mobile, answered in seconds.

Tender teams

Prior bid content and compliance requirements, retrieved under deadline pressure.

Answers you can audit

Every response traces to a source document. No citation, no answer — that's how enterprise RAG should work.

Book a Consultation

FAQ

Frequently Asked Questions

What is RAG (Retrieval-Augmented Generation)?

RAG is an AI architecture where the system first retrieves relevant passages from your own documents, then generates an answer grounded in those passages — with citations. It lets AI answer from private, current knowledge without training on it.

Should we use RAG or fine-tune a model?

For enterprise knowledge, RAG almost always wins: instant knowledge updates, native citations, permission enforcement at retrieval time, and lower cost. Fine-tuning suits style/format adaptation or narrow classification tasks — not knowledge storage.

How do you prevent hallucinated answers?

Three layers: permission-aware retrieval over clean data, answers constrained to retrieved passages with inline citations, and an evaluation set scored before launch and after every change. If no source supports an answer, the assistant says so instead of guessing.

How much does a RAG implementation cost?

A focused single-source assistant typically runs $20K–$100K including ingestion, evaluation, and integration. Multi-source, permission-complex deployments range $100K–$300K+. Data preparation dominates the budget, as with all AI projects.

How long does implementation take?

A focused pilot: 6–12 weeks including data ingestion, retrieval tuning, evaluation, and a controlled rollout. Multi-source enterprise deployments: 3–6 months.