Generate and Qualify Leads with AI Chatbots

Many marketing teams start with platforms like Intercom or Drift. These tools are excellent for routing conversations but their lead qualification relies on simple, rule-based branching logic. It is a decision tree, not a conversation. The bot can ask for company size and budget, but it cannot infer a lead's seniority or purchase intent from the technical sophistication of their questions.

For example, consider a SaaS company using a standard chatbot. A visitor asks, "Can your API handle 100 requests per second with a p99 latency under 250ms?" The rule-based bot sees the keyword "API" and sends a link to general API documentation, completely missing the high-intent signals. The prospect, an engineer or technically-savvy buyer, gets a low-value answer and bounces. The bot failed to identify and score a lead who just revealed specific, critical purchase criteria.

Other tools like Tidio are designed for customer support automation and small-scale sales. They cannot maintain context over a long conversation or dynamically generate follow-up questions. If a lead provides a nuanced response that does not fit a pre-programmed path, the bot breaks down and defaults to "let me get a human." This forces a delay, especially after hours, and you lose the opportunity to qualify the lead while their interest is highest.

The structural problem is that these off-the-shelf platforms are closed systems. You cannot replace their underlying language model with a more powerful one like Claude, nor can you inject custom Python code to execute complex scoring logic. They are built to serve thousands of customers with generic features, not to execute the specific lead qualification strategy that defines your sales process.

The first step is a discovery workshop to define your ideal customer profile and lead qualification criteria. Syntora would work with your team to map the key questions, intent signals, and disqualifiers that separate a great lead from a time-waster. This produces a detailed scoring rubric that becomes the bot's core logic, ensuring the AI is optimized for your specific business goals, not a generic template.

The technical approach would use a FastAPI service to manage the conversation state. Each message from the user is sent to the Claude API with a structured prompt containing the conversation history and the scoring rubric. This allows the bot to ask intelligent, context-aware follow-up questions. We would use Python for the entire stack, Supabase for logging conversations and lead scores, and deploy the system on AWS Lambda for an operational cost typically under $50 per month.

The delivered system integrates with your existing website chat widget. When a lead's score crosses a predefined threshold (e.g., 85 out of 100), the system automatically pushes the contact, the full transcript, and an AI-generated summary to your CRM. You receive the complete source code, a runbook for making minor prompt adjustments, and a monitoring dashboard to track qualification rates and bot performance.