AI Chatbot Development

Who this is for

• Support teams answering the same questions hundreds of times a month
• Products adding an in-app assistant that actually knows the product
• Businesses that want a lead qualifier working around the clock
• Teams burned by a chatbot that confidently made things up

What you get

• A production chatbot answering from your documents and data (RAG)
• Escalation to a human with full conversation context when it can't answer
• Guardrails against off-topic, unsafe and invented answers
• Web widget, in-app or WhatsApp deployment
• An admin view of conversations, unanswered questions and knowledge gaps
• Cost per conversation instrumented from day one
• 30 days of post-launch support

Grounded in your data, not the model's memory

A model answering from its training data will eventually invent your refund policy. The fix is retrieval-augmented generation: the bot searches your actual documents, answers only from what it found, and cites the source. That moves the quality question from 'how smart is the model' to 'how good is the retrieval' — chunking, embeddings and reranking tuned to your content — which is engineering you can measure and improve, rather than behaviour you can only hope for.

The escalation path is a feature, not a failure

The worst chatbot answer is a confident wrong one; the second worst is a dead end. When the bot cannot answer from its sources, it says so and hands the conversation to a human — with the full transcript and the retrieved context attached, so the customer never repeats themselves. Every unanswered question is also logged and reported, which turns the bot into an instrument that shows you exactly where your documentation is thin.

Where the bot lives matters

A website widget suits support and lead capture; an in-app copilot can see the user's context and act on it; WhatsApp meets customers where they already are, which matters in the Gulf and India in particular. The channel decision shapes the build — session handling, identity, what the bot is allowed to know about the person typing — so it is made at design time, not bolted on after the widget ships.

Cost per conversation is a design decision

Token spend is architecture. Routing routine questions to a cheaper model and hard ones to a capable one, caching stable prompt prefixes, and retrieving passages instead of stuffing whole documents into context routinely changes the monthly bill by an order of magnitude. Every conversation is instrumented for cost from day one, so you see the number per conversation on a dashboard — not as a surprise on an invoice.

How the engagement works

1. 01

   Discovery

   Identify the questions the bot must answer, the sources of truth for them, and the conversations that must go straight to a human.

2. 02

   Design

   Define the retrieval sources, tone, refusal behaviour and escalation rules, with an evaluation set of real questions built alongside.

3. 03

   Build

   Implement retrieval, prompting and the interface, then test against the evaluation set and a pilot group before public launch.

4. 04

   Ship

   Launch with conversation analytics, cost tracking and gap reporting, plus 24-hour response times for 30 days after launch.

Technology

Models

The OpenAI, Claude and Gemini APIs, routed by task — a capable model for hard questions, a cheaper one for routine ones.

Retrieval

Chunking, embeddings and reranking tuned to your documents; Supabase pgvector or Convex for the index, LangChain for the plumbing.

Channels

An embeddable web widget, native in-app integration, or WhatsApp via the Business API.

Quality

An evaluation set of real user questions, hallucination checks against sources, and analytics on unanswered queries so the knowledge base grows where it is weak.

Proof

Frequently asked