How We Built an AI Chatbot That Replaces Contact Forms
How We Built an AI Chatbot That Replaces Contact Forms
Contact forms are a dead end. Visitors fill them out, wait for a response, and most never hear back quickly enough. The conversion rate on a typical B2B contact form is around 2-3%. The rest of those visitors leave and never come back.
When we set out to build b2blead.ai, the goal was specific: replace static contact forms with an AI conversational layer that engages visitors instantly, answers their questions using the company's own content, and qualifies leads before a human ever gets involved.
This post covers the technical approach we took and the decisions that shaped the final product.
The Problem
B2B companies invest heavily in driving traffic to their websites. But when a visitor arrives with a question, they encounter a form that says "We'll get back to you within 24-48 hours." By then, the visitor has already contacted three competitors.
The gap between visitor intent and business response creates a massive leak in the sales funnel. AI can close that gap.
Architecture Decisions
We built b2blead.ai as a multi-tenant SaaS platform — not a one-off chatbot. This meant designing for hundreds of concurrent deployments, each with their own knowledge base, branding, and integrations.
Knowledge Retrieval with RAG
The core of the system is retrieval-augmented generation (RAG). For each client, we:
- Ingest their website content — pages, blog posts, documentation, product descriptions
- Chunk and embed the text into a vector database
- At query time, retrieve the most relevant passages based on the visitor's question
- Generate a response grounded in the client's actual content, not generic LLM knowledge
This means the chatbot gives accurate, company-specific answers rather than hallucinated general information. When a visitor asks "Do you offer same-day delivery in Bangkok?", the system finds the relevant shipping policy page and responds with the actual answer.
