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AI Chatbots

Chatbots that answer from what you actually know.

A general model will answer anything, confidently, including things it has no basis for. Our AI chatbot development services build conversational AI assistants grounded in your own content with RAG and wired into your systems, so answers are traceable to a source your team trusts.

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What We Build

Grounded, integrated, and honest about limits.

The hard part of a useful chatbot is not the conversation. It is retrieval quality, permission handling, and knowing when to say it does not know. That is exactly where generic chatbot products tend to fall down.

Grounding

Answers with a source

Retrieval over your own documentation and data, so every answer can be traced back to where it came from.

Privacy

Your content stays yours

Your data is never used to train external models, and permission boundaries are respected at retrieval time, not just in the UI.

Integration

Connected to real systems

The assistant can look things up in the systems that hold the answer, rather than guessing from a stale document dump.

Honesty

Knows what it does not know

Tuned to say so when the knowledge base does not support an answer. A confident wrong answer costs more than no answer.

How We Build It

Five stages from problem to production.

The same process across every AI Development project, scaled to the size of the problem.

01

Discovery

We work out what the system actually has to do, what data exists, and what happens today when it goes wrong.

02

Design

Architecture, model choice, integration points and failure handling, defined before any of it gets built.

03

Integration

Connecting to the systems that hold your data, with security and permission boundaries handled properly.

04

Automation

The system takes on real work, in the workflows your team already uses rather than beside them.

05

Refine

Tuned against real usage and measured with evals, because how people use a system is never quite how it was designed.

Related Work

Systems we have shipped.

Conversational AI Studio
AI DevelopmentAI Chatbots

Conversational AI Studio

Context-aware multi-channel chat that qualifies inbound leads and routes them to the right person.

3.2× qualified leads, 54% faster replies
 Support Hub Inbox
AI DevelopmentAI Automation

Support Hub Inbox

One inbox across web chat, email, WhatsApp and Facebook, with AI replies trained on past resolutions.

60% faster replies, 99.95% uptime
 AI Marketplace Platform
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AI Marketplace Platform

Support chatbot, live SEO analyser and a vector recommendation engine, all sharing one retrieval layer.

+10% GMV, 40% fewer support tickets
See all case studies
What Teams Say

Hear from the teams we work with.

“We needed a voice agent that could actually qualify leads, not a chatbot pretending to be one. The team shipped a sub-700ms pipeline in 6 weeks. It now handles 5k calls a day.”
Priya RajHead of Growth, Ninjatech
“What sold us was their willingness to put AI engineers and product designers on the same call. We got working prototypes by week two and a production rollout in three months.”
James ThorntonCTO, Allindex
“AGI designed a CRM system tailored to our client management process. It is intuitive, reliable, and has centralized all our communication and history in one dashboard. This has greatly improved client retention.”
Carlos MendesProduct Manager, Qilinlab
Common Questions

Before you get in touch.

Will it train on our data?

No. Your content is used for retrieval at query time, not to train an external model. That distinction matters for both accuracy and confidentiality.

Can it respect who is allowed to see what?

Yes, and it should. Permissions are applied during retrieval, so a user never gets an answer synthesised from a document they could not open themselves.

What if it does not know something?

It says so. That behaviour is a design decision and a tuning target. An assistant that bluffs is worse than no assistant, because people stop being able to trust any of its answers.

What is RAG, and do we need it?

Retrieval augmented generation means the chatbot answers from your own documents and data, retrieved at the moment the question is asked, instead of from whatever the model happened to memorise during training. If answers have to be traceable to a source your team trusts, you need it. It also means no external model is trained on your content.

Talk to us about ai chatbots.

Tell us what the system would need to do and what it is replacing. We will tell you whether it is worth building and roughly what it takes.

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