Generative AI & Chatbots
Conversational AI that understands your domain. From a support assistant grounded in your own content to full RAG pipelines, we ship assistants that give accurate answers instead of confident guesses.
What this service is
A general-purpose AI model knows a lot about the world and nothing about your business. We close that gap with retrieval-augmented generation (RAG): the assistant looks up the relevant parts of your own documents, policies and product data before it answers, and can point to where the answer came from.
We deploy the assistant where your users already are — your website, WhatsApp or your own app — and connect it to your support team, so a conversation that needs a person reaches one with the full context. After launch we keep tuning prompts, retrieval and cost against real conversations.
What we deliver
- Retrieval-augmented assistants grounded in your content
- Web, WhatsApp and in-app chat channels
- Escalation to human agents with full context
- Prompt and cost tuning after launch
Typical stack
Where it helps
Customer support assistant
Answers common questions around the clock from your help content and hands harder cases to your team.
Document Q&A
Lets users ask questions of manuals, contracts, reports or scanned PDFs and get answers drawn from those documents.
Education and training
Tutor-style assistants that explain course material and answer student questions at any time.
Summarisation
Condenses long reports, transcripts and videos into short summaries and key takeaways.
How we work
A delivery model built around short feedback loops, so you are never waiting months to find out whether something works.
- 01
Discover
We start with the problem, not the technology. A short workshop to map the workflow, the constraints and what success actually looks like in numbers.
- 02
Prototype
A working proof of concept on your real data, usually within two to three weeks, so you can judge feasibility before committing to a full build.
- 03
Build
Delivery in two-week increments with a demo at the end of each one. You see progress continuously and can change direction while it is still cheap.
- 04
Support
Monitoring, tuning and iteration after launch. AI systems in particular need measurement over time — we stay on to make sure quality holds.
Frequently asked questions
How do you stop the chatbot from making things up?
We ground every answer in your own content with retrieval, instruct the model to say when it does not know, and test it against real questions before launch. Questions it cannot answer reliably are routed to a person.
Which channels can the chatbot run on?
Your website, WhatsApp and in-app chat are the most common. The same assistant can serve several channels at once.
How does a project start?
With a short discovery workshop. We map the workflow, the constraints and what success looks like in numbers, then come back with a practical scope, a realistic timeline and a clear price.
Can we see something working before committing to a full build?
Yes. We build a proof of concept on your real data, usually within two to three weeks, so you can judge feasibility before signing up for the full project.
How will we see progress during the build?
We deliver in two-week increments with a demo at the end of each one, so you can change direction while it is still cheap to do so.
Do you support the system after launch?
Yes. We stay on for monitoring, tuning and iteration after launch, so quality holds as your data and usage change.
Talk to us about Generative AI & Chatbots
Tell us what you are trying to build. We will come back with a practical scope, a realistic timeline and a clear price — usually within two working days.
Start a conversation