From first target
to closed deal.
An AI-native revenue platform for businesses that sell through conversations. Build audiences, run outbound, answer every WhatsApp and email in one inbox, qualify with agents you can actually govern, and connect it all back to revenue.
Replacesa shared WhatsApp number
No sign-up. Real product, seeded with demo data.
Built on
How it works
One inbox, one pipeline, one answer to “what worked?”
Answer
Every conversation lands in one governed inbox
WhatsApp and email in a single queue with ownership, SLA timers and an agent that qualifies — until a human takes over, explicitly.
- The agent asks, captures and knows when to stop
- Collision detection — two people never answer the same thread
- Hindi and English are both first-class
Arjun Deshpande
FOB. First shipment in about 3 weeks
Meera Iyer
Can you send the revised quote today?
Faisal Khan
What's the transit time to Rotterdam?
Qualify
Deals move only when the fields say they can
Qualification against fields you define, a lifecycle that refuses illegal moves, and a board that shows who owns what.
- Stages and fields are configuration, not code
- Scoring you can read rule by rule
- Approvals for anything that spends money
Qualified2
Sundaram Textiles
₹4.2L
Apex Polymers
₹1.8L
Proposal1
Veda Interiors
₹2.4L
Kohinoor Freight
₹3.1L
Attribute
Revenue walks back to the touch that caused it
Attribution is computed over the timeline itself — ad click to cold email to WhatsApp reply — and frozen the moment a deal closes.
- Cost per qualified lead, not cost per form fill
- One person, many addresses, one record
- Closed history never moves under a model change
Revenue by first touch
₹18.6L
One graph, end to end
Every stage walks back to the touch that caused it
Most stacks lose the thread somewhere between the ad click and the closed deal. Here the whole chain is one query over one graph, so “which campaign produced this revenue” has an answer rather than an estimate.
Audience
Built from filters or imported
18,400Touched
Cold email, ads, click-to-WhatsApp
6,820Conversation
A real reply, on either channel
612Qualified
Against your own definition
209Opportunity
In the pipeline, owned
63Won
Attributed back to the first touch
17
Five surfaces, one system
Not five products stitched together — they share a contact, a consent record and a timeline, which is the only reason the attribution above is possible.
See it with data in it
The demo is the real product, seeded with two businesses that sell completely differently. Switch between them and watch every configured screen change.
Governed, not hopeful
Rules are enforced in code, not requested in a prompt
An instruction in a system prompt is a suggestion the model may or may not follow. Here the rules live outside the model, at three enforcement points: a gate before any tool runs, a gate before anything is sent, and a verifier on every reply.
- The agent never denies being an AI — refused by the verifier, not asked for in the prompt
- Every outbound message passes a consent, suppression and sending-tier check
- Anything that spends money is surfaced for approval, never applied silently
Never quote a rate before the lane and volume are captured
Tool refusedThe pre-tool gate blocks it. Not a prompt instruction — a refusal.
Hazardous cargo always goes to a human
EscalatedForced handover on topic, before the agent can answer.
Discounts above 10% need manager approval
Approval requiredLands in the approvals queue with the number attached.
“The agent asks, captures, and knows when to stop. The handover to a human is explicit — and that is the whole trick.”
One person, many addresses, one timeline
Somebody clicks an ad, replies to a cold email, then messages your WhatsApp number from a phone you have never seen. Most systems make that three records. Here it is one.
- 1
Ad click
Meta lead form, 3 months ago
- 2
Cold email
Sequence step two, opened twice
- 3
WhatsApp reply
New number, matched by signed token
- 4
Closed won
₹2.4L, attribution frozen at close
Built for businesses that qualify before they quote
Nothing here is hardcoded to a vertical. What you capture, the stages you move through, the rules the agent obeys — all configuration, which is why two businesses that sell completely differently run on the same system with no bespoke code.
See it running, with data in it
The demo is the actual product. Two seeded businesses, real conversations in English and Hindi, and every governance rule enforced exactly as it would be in production.