NeverLose
An AI sales agent that works on hesitant buyers in real time. It reads 11 buyer signals and runs an escalating negotiation before the customer leaves. Two-sided: a customer-facing sales agent plus a merchant intelligence copilot on shared infrastructure. Finalist (Top 5 of 90+) at the Pine Labs AI Hackathon.
Problem
E-commerce loses hesitant buyers silently: they stall at checkout, flinch at price, and leave without a trace. Static discounts leak margin on buyers who would have converted anyway, and merchants have no visibility into why deals die.
Approach
- Detect hesitation in real time across 11 buyer signals: exit intent, price-shock prediction, cart stall, and more.
- Run a three-level escalating negotiation with a live deal-expiry countdown to convert before the customer leaves.
- Built it two-sided: a consumer-facing sales agent plus a merchant intelligence copilot on shared infrastructure.
- Orchestrated multi-agent architecture on Claude: Sonnet supervisor coordinating Haiku sub-agents for latency.
Outcome
A working two-sided platform built end-to-end at the Pine Labs AI Hackathon. Finalist (Top 5 of 90+ teams) with 11 production buyer signals detected live.
Architecture
state = detect_signals(session) # 11 signals
if state.exit_intent or state.price_shock:
offer = negotiate(
level=escalate(state),
deadline=countdown(90),
)
merchant.copilot.log(state, offer)Highlights
Real-time hesitation detection across 11 buyer signals (exit intent, price-shock prediction, cart stall, and more).
Three-level escalating negotiation with a live deal-expiry countdown.
Two-sided platform: consumer sales agent + merchant copilot on shared infrastructure.
Multi-agent architecture on Claude (Sonnet 4.6 supervisor + Haiku sub-agents).
Finalist (Top 5 of 90+) at the Pine Labs AI Hackathon.
Stack
- Next.js
- TypeScript
- FastAPI
- Anthropic Claude
- MongoDB
- Websockets
- Large Language Models