Capture every enquiry from email, WhatsApp and web forms, qualify it with AI, write it to your CRM and route it to the right person, with approval on anything external.
The problem
Leads arrive across email, WhatsApp, forms and calls. Someone copies them into the CRM by hand, qualification depends on who picks it up, and the fastest responder wins the deal.
How it works
Every enquiry captured, qualified and routed with context, in minutes rather than days.
- CaptureThe intake agent reads new enquiries from every channel you connect.
- EnrichIt checks the CRM for an existing account and fills in what it can find.
- QualifyIt applies your qualification criteria and records the reasoning.
- RouteThe lead goes to the right owner with a summary and a drafted reply.
- ApproveYour team approves the reply or the booking before it is sent.
Where people stay in control
- External replies and meeting invitations need approval
- Disqualification can be reviewed and overridden
- Pricing and commitments stay with sales
What it connects to
How we deliver ai lead qualification
The same method as every AI Agents & Automation engagement: each stage ends with a decision you sign off. We work with teams in Bengaluru, across India and abroad.
We study the problem with the people who live it: workflow, customers, constraints.
Problem brief agreedWe agree what success means, what is in scope and the hypothesis we are testing.
Scope signed offFlows, interfaces and architecture, tested as a prototype before engineering starts.
Prototype approvedWorking software and artefacts, demonstrated every week.
Features acceptedRelease to real users with documentation, monitoring and a runbook.
Release acceptedWe measure what happened and turn it into the next roadmap.
Roadmap reviewedRelated insights
- What is an AI-native company?An AI-native company redesigns workflows, decisions, data access and software so AI is part of how the business runs. What it means, how it differs from adding AI tools, and how to start.
- What should an AI agent be allowed to do? A practical modelA practical permission model for AI agents: four levels of action from read-only to high-impact, how to enforce them in code rather than prompts, and how to design the handoff to people.
- The anatomy of a production AI agentKnowledge, tools, permissions, a provider abstraction, evaluation and observability: the parts that turn an AI demo into a production AI agent a business can rely on.
Can AI qualify sales leads accurately?+
It can apply your documented criteria consistently and explain its reasoning. Accuracy depends on clear criteria and good CRM data, which is why we start by mapping how your best people qualify today.
Will the agent reply to customers on its own?+
Only if you choose that. By default, external messages are drafted for approval, and the permission level can be widened as confidence grows.
Related use cases
AI customer support agent
Routine questions answered from approved knowledge; complex cases handed over with the full thread.
AI voice agent
Calls answered, requests captured and slots booked, with a transcript for every conversation.
AI document processing
Fields extracted with citations, uncertain items flagged, and a reviewer in control.