Retrieval-augmented generation connects a language model to your documents, help centre and data so answers are specific, sourced and easier to trust.
What this helps you achieve
Instant, sourced answers
Responses reference the documents they came from.
Captured leads
Conversations can collect contact details and book calls.
Less repetitive support
Routine questions are handled so your team can focus on complex cases.
Insight into questions
See what people ask and where your content has gaps.
About this service
A chatbot answers questions in a chat window, on WhatsApp or inside your product. On its own, a language model does not know your business. Retrieval-augmented generation (RAG) solves that: when someone asks a question, the system first searches your own documents, help articles or product data, then asks the model to answer using only what it found.
The answer can show its sources, and the bot can say "I am not sure" and pass the conversation to a person. We also test the bot on a set of real questions before launch, so quality is measured rather than assumed.
1.Question
A visitor asks in chat or on WhatsApp.
2.Search
Relevant passages are found in your content.
3.Answer
The model replies using only those passages.
4.Show sources
Links to the original documents are included.
5.Hand over
Unclear or sensitive questions go to a person.
Does this sound familiar?
Customers and staff repeatedly ask things your documentation already answers.
Policies and know-how are buried in drives, wikis and old threads.
A chatbot without your data will confidently invent details.
Visitors leave when nobody replies, and leads go cold.
What's included
Import websites, PDFs, docs and databases, kept up to date as content changes.
Responses grounded in retrieved passages with citations.
Embed on your site, or deploy to staff tools.
Escalate to a person and log leads and conversations in your CRM.
Serve customers in more than one language where the underlying model supports it, tested on your content.
Restrict content by audience and review usage and feedback.
How it works
A focused call to understand your goals, tools, constraints and what success looks like.
We scope the solution, agree priorities and share a written plan before any build starts.
We ingest your content, tune retrieval and answer quality against real questions, then build the interface.
We deploy, monitor real usage, document the system and iterate based on what we learn.
Business benefits
Responses are based on your documents and policies, not guesses.
Common questions are answered at any time, and harder ones are queued for your team.
Visitors and staff can open the document an answer came from.
Conversations pass to a person with the full history when needed.
Update a document and the bot's answers follow, with no retraining.
Practical examples
Illustrative scenarios that show how we approach typical problems. They are examples, not descriptions of specific clients or guaranteed results.
Tools & technologies
We choose tools to fit your systems and team. Links go to each tool's official documentation.
OpenAI API
Language models for classification, extraction and drafting.
Official documentation for OpenAI API (opens in a new tab)Claude API
Language models with strong long-document and tool-use support.
Official documentation for Claude API (opens in a new tab)pgvector
Vector search inside PostgreSQL for retrieval-augmented answers.
Official documentation for pgvector (opens in a new tab)PostgreSQL
Relational database for structured business data.
Official documentation for PostgreSQL (opens in a new tab)Next.js
Web applications with server rendering and fast page loads.
Official documentation for Next.js (opens in a new tab)Node.js
Back-end services, APIs and background jobs.
WhatsApp Business Platform
Customer messaging on WhatsApp through the Cloud API.
Official documentation for WhatsApp Business Platform (opens in a new tab)Use cases
Examples of where businesses typically begin. Your best first project depends on your workflow.
Answer product, pricing and policy questions from your help content.
Help staff find policies, procedures and past project details.
Guide visitors to the right product or service.
Qualify visitors and book consultations automatically.
Walk new customers or employees through setup.
Ask questions of contracts, reports and manuals.
Related work
Built products and clearly labelled illustrative examples related to this service.
FAQ
Retrieval-augmented generation (RAG) first searches your own content for relevant passages, then asks the language model to answer using only those passages. This makes answers more specific and lets the bot show its sources.
Any language model can be wrong. We reduce the risk by restricting answers to retrieved content, instructing the bot to say when it does not know, testing on real questions and giving you analytics to spot weak answers.
Modern models handle many languages, including Arabic. We test answer quality on your actual content before launch rather than assuming.
We agree up front where documents are stored and which model providers process them, and can use providers and settings that fit your privacy requirements.
Yes. The bot can escalate to email, live chat or your CRM, passing along the conversation so nobody has to repeat themselves.
Learn more
Five illustrative n8n workflows for small businesses, from lead triage to weekly reporting, with architecture, implementation steps, security limits and licensing notes.
Learn how AI agents differ from chatbots, when tool use helps, how to test an agent, and which permissions and safety checks to apply before using one.
Learn Claude Code setup, model choices, context management, subscription versus API billing, token costs, and practical ways to reduce usage safely.
Book a consultation and we'll help you work out scope, approach and a sensible first step.
Explore other services
