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Internal Engineering Demonstration

AI Lead Qualification Agent

Internal engineering demonstration. This is not a client deployment. All scenarios, figures and outcomes are illustrative and created to showcase NorthFlow Studio's technical approach.

Business challenge

  • Inbound leads arrived across form, email and WhatsApp with no consistent triage.
  • Sales reps spent time on low-fit leads while hot ones went cold.
  • No single score to prioritise follow-up.

Objectives

  • Lead classification latency: Automated classification within seconds
  • Hot lead routing: Immediate routing with full context attached
  • Manual triage reduction: Significant reduction in manual rep time spent on triage

Architecture

Lead qualification agent
Form / Inbox │ ▼ [ Classify ] ──▶ spam? → discard │ ▼ [ Extract ] ──▶ name, need, budget, timeline │ ▼ [ Score ] ──▶ fit 0–100 │ ├─ hot ──▶ notify human + create CRM deal └─ warm ──▶ enroll in nurture sequence

Inbound lead → agent → scored + routed.

score.ts
export function score(lead: Lead): number {
  let s = 0;
  if (lead.budget) s += 40;
  if (lead.timeline === "now") s += 30;
  if (lead.fit) s += 30;
  return Math.min(100, s);
}

Technology

TypeScriptOpenAISupabasen8nWhatsApp Business API

Features

  • Multi-channel ingestion (web form, email, WhatsApp) into a unified schema
  • LLM-based entity extraction with typed JSON validation
  • Rule-validated scoring engine (budget, timeline, fit) with override logging
  • Automated nurture sequence enrollment for warm leads
  • CRM deal creation with extracted lead context attached

Implementation

  1. 1Ingest leads from each channel into one normalized table.
  2. 2Run a classification + extraction step with a typed schema.
  3. 3Score fit and route hot leads to a human with context attached.
  4. 4Enroll warm leads in an automated nurture sequence.

Tradeoffs

Model vs rules
LLM extraction is flexible but needs validation; we keep a rules layer for hard constraints.
Latency
Real-time scoring adds ~1s; acceptable for lead routing, not for page loads.

Performance

  • p95 classification under 1.5s.
  • Batched nurture sends to protect deliverability.

Lessons learned

  • Validation matters more than the model — bad extraction ruins routing.
  • A simple score beats a clever one if sales trusts it.

Future roadmap

  • Add learning from rep overrides to improve the score over time.
  • Support more channels via a single ingestion adapter.
Demo video placeholder
Screenshot / GitHub placeholder

FAQ

Is this a client deployment?+

No. It is an internal engineering demonstration illustrating our approach. Figures are illustrative.

Can you build this for us?+

Yes — see AI Agents and Business Automation, or book a call.

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