releadr.com · AI Lead-Recovery Engine
A multi-tenant SaaS concept that re-engages the leads a business has already given up on — running short, AI-assisted, human-in-the-loop conversations across SMS, WhatsApp, and Email, then writing the outcome straight back into the systems the business already runs. Designed and prototyped end-to-end by quickdev.
The lead already raised a hand.
Nobody followed up in time.
Service businesses lose a striking share of revenue not because customers say no, but because nobody gets back to them in time. A quote goes out and goes quiet. A form comes in after hours. A "let me think about it" never gets a second touch. The intent was there — the follow-up wasn't.
Doing it by hand doesn't scale: it's late, inconsistent, limited to office hours, and the moment a conversation needs a missing detail — a date, an address, a budget — it stalls. Meanwhile every business runs a different stack. Some have a CRM with webhooks, some only a system you can poll, some still live in spreadsheets.
The concept question was simple: could a single orchestration layer sit on top of whatever a business already uses, revive the leads worth reviving through a real conversation, and stay safely inside the lines a business owner would actually trust an automated agent to operate within?
Most "lost" leads were never really lost — they were just never followed up with. releadr's job is to continue the conversation, collect what's missing, and either book the job or hand a warm lead back to a human — without ever going off-script.
A conversation engine that
stays on the rails.
We designed releadr as a pluggable orchestration layer, not another CRM. Leads arrive however a tenant can send them — inbound webhook, scheduled pull, file import, or a partner relay from a tool like Zapier or Make — and are normalized into one tenant-scoped lead and conversation model.
From there a state machine, not the model, drives the conversation. The AI layer interprets intent, extracts entities, and detects objections; templates and tenant policy decide what can actually be said. Low-confidence or policy-sensitive turns route to a human for approval before anything goes out — so a business can pilot in a fully supervised mode and dial up automation only as trust builds.
Every outcome — recovered, needs info, callback requested, booked, or handoff — is written back through whichever outbound path the tenant prefers: real-time push, scheduled batch, a pull feed, or an operator task queue. The whole surface is configuration-driven, so onboarding a new tenant never means a new code branch.
- Pluggable lead ingestion — inbound webhook, scheduled pull, file import, and partner relay, several active at once per tenant
- Normalized lead + conversation-session model with tenant-scoped trigger and suppression rules
- State-machine-controlled conversation with an AI layer for intent, entity extraction, and objection detection
- Human-in-the-loop approval path with real-time review notifications for low-confidence cases
- Configurable outbound write-back — real-time push, scheduled export, pull feed, or operator task queue
- Admin dashboard for field mapping, templates, taxonomy, discount policy, hours, escalations, and sync monitoring
- Multi-tenant isolation with per-tenant secrets, audit trail, trial spend caps, and Hebrew/English (RTL) support
Architected for trust, isolation, and scale.
A working prototype
for a revenue problem every service business has.
- A complete product concept taken from PRD and conversation design through to a working .NET + Angular prototype
- Safety designed in from the start — the AI suggests interpretation, never policy; humans approve anything sensitive before it sends
- Genuinely integration-agnostic: the same engine fits a tenant on modern webhooks or one still exchanging files
- Multi-tenant isolation, audit trails, trial spend caps, and pre-go-live security reviews built into the architecture, not bolted on
- A reusable blueprint for how quickdev approaches AI products — constrained, auditable, and shipped with a human in the loop
yours?
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