RAG Chatbots That Answer From Your Trusted Business Knowledge.
We build Retrieval-Augmented Generation chatbots that ground every answer in your approved documents — with citations, access controls, and evaluation baked in.
Custom chatbot development for businesses across the USA and UK · Response within one business day
Retrieve first, then answer.
A RAG chatbot doesn't just guess based on what an LLM learned two years ago. It looks up the relevant passage in your knowledge base first, then writes an answer using that passage. The result: current, grounded, and traceable.
Under the hood
In plain language: your documents are broken into chunks, each chunk is turned into a numeric vector (an embedding) that captures its meaning, and stored in a vector database. When a user asks a question, we embed the question too, find the closest-matching chunks, and hand them to the LLM as reference material.
- Embeddings: numeric fingerprints of meaning
- Vector search: find the closest fingerprints
- Reranking: pick the truly best matches
- Grounded prompting: LLM writes using those matches
- Citations: link back to the source passage
Supported data sources
Any content you can share, we can turn into an answerable knowledge base.
Grounded, safe, and easy to maintain.
Grounded in your data
Answers come from your approved documents, help articles, CRM records, and internal wikis — not the model's training data.
Source citations
Every answer can link back to the passage it came from. Users can verify; you can trust.
Access-controlled
Documents respect user permissions. Employees only see what they're allowed to see.
Easy knowledge refresh
Update a doc — the chatbot's answer updates too. No retraining, no long delays.
Hallucination guardrails
Confidence thresholds, retrieval quality checks, and fallback to "I don't know" instead of making things up.
Clean human handoff
When retrieval quality is poor or the question is out of scope, the bot escalates to a person with full context.
Which one fits your use case?
For most business chatbots, RAG is the right tool. Fine-tuning wins for teaching a model specific behaviours. Both can be combined when needed.
| Feature | RAG | Fine-tuning |
|---|---|---|
| Best for | Changing company information, docs, help content, product data | Model behaviour — tone, output format, classification tasks |
| Data location | External knowledge sources retrieved at query time | Training examples baked into the model |
| Update speed | Update source content — the bot updates itself | Requires retraining when the data changes |
| Source references | Yes — answers can cite passages | No — the model gives an answer, not a source |
| Typical cost | Lower — no training compute needed | Higher — training + retraining costs |
| Best use case | Documentation, support, sales assistants | Consistent tone, specialised output formats, classification |
Beyond blue links.
| Feature | Traditional search | RAG chatbot |
|---|---|---|
| Response format | List of blue links | A synthesised answer, with citations |
| Query style | Keyword matching | Natural language, follow-up questions |
| Handles ambiguity | Weak — surfaces top matches | Asks clarifying questions when needed |
| Integrates actions | No | Yes — book, update, escalate from the same UI |
Beyond decision trees.
| Feature | Rule-based chatbot | RAG chatbot |
|---|---|---|
| Data used to answer | Fixed decision trees | Your live knowledge base |
| Unknown questions | Dead-ends or misroutes | Grounded best answer, or honest "I don't know" |
| Maintenance | Rebuild flows manually | Update the source content |
Where RAG delivers the highest leverage.
Customer support assistant
Deflect L1 tickets by answering from help centre, product docs, and past resolved tickets.
Sales enablement assistant
Instant answers for prospects from pricing, feature docs, case studies, and competitor comparisons.
Internal knowledge assistant
Employees ask questions across policies, wikis, and tooling — with permissions respected.
Compliance / policy assistant
Answer questions grounded strictly in approved policy documents, with citations for audit.
How we build a RAG chatbot.
- 01
Discovery
Which questions matter, which sources answer them, who's allowed to see what.
Deliverable: Written scope + architecture
- 02
Data preparation
Ingest, clean, chunk, and embed your sources. Set the refresh cadence.
Deliverable: Indexed knowledge base
- 03
Retrieval design
Hybrid search (keyword + vector) + reranker + permission filter. Tune for recall + precision.
Deliverable: Retrieval quality report
- 04
Answer generation
Grounded prompting, citation format, confidence thresholds, fallback rules.
Deliverable: Prompt spec + guardrails
- 05
Evaluation
Automated eval suite on real questions. Groundedness, correctness, safety, latency, cost.
Deliverable: Eval baseline
- 06
Deploy + monitor
Production launch with logging, dashboards, and a workflow to fix bad answers fast.
Deliverable: Live RAG chatbot
RAG chatbot FAQ.
Ground your chatbot in the knowledge your business already has.
Tell us about your customer questions, business documents, and existing systems. We'll recommend a practical chatbot solution based on your requirements.
No-obligation consultation · Your information stays confidential · Response within one business day.