leadflow-ai

AI Lead Qualification & Automation Pipeline

A modular AI system for analyzing, scoring, and activating inbound leads using LLMs, structured outputs, and rule-based logic.

This project demonstrates how to transform raw lead data into actionable business decisions: prioritization, personalized responses, and automated routing — with a built-in safety layer.

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Pipeline

ingest → sanitize → analyze → score → decide → respond → activate

• ingest: lead data from forms, CSV, CRM
• sanitize: input cleaning + prompt injection detection
• analyze: LLM extracts intent, needs, use cases
• score: deterministic scoring logic
• decide: priority assignment (A / B / C / Manual Review)
• respond: personalized email generation
• activate: simulated CRM push + outreach actions

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Features

• LLM-based lead analysis (intent, maturity, use cases)
• Deterministic scoring & prioritization
• Context-aware email generation
• Prompt injection detection & safety routing
• GDPR-conscious data handling (email masking)
• Batch processing from CSV or dataset
• Activation layer (CRM + outreach simulation)

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Example Output

Lead	Score	Priority	Action
High-intent SaaS lead	95	A	Book call
Exploratory lead	50	B	Qualification
Low-intent	38	C	Nurture
Suspicious input	0	Manual Review	Block automation

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Safety Layer

This system treats all lead inputs as untrusted data.

Includes:

• prompt injection detection
• structured output validation (Pydantic)
• type-safe parsing (list normalization)
• security flag propagation
• manual review routing (no automation for risky leads)

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Example Use Case

df = pd.read_csv("leads.csv")
results = [process_lead(lead) for lead in df.to_dict(orient="records")]
pd.DataFrame(results)

Then:

• send emails to Priority A leads
• push qualified leads to CRM
• route flagged inputs to manual review

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Architecture (Conceptual)

Form / LinkedIn / CRM ↓ Ingestion Layer ↓ Safety Layer ↓ LLM Analysis (OpenAI) ↓ Scoring Engine ↓ Decision Layer ↓ Activation Layer (Email / CRM / Routing)

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Production Integration (Example)

• Make / n8n → workflow orchestration
• OpenAI API → analysis & generation
• Airtable / HubSpot → CRM storage
• Gmail / Slack → notifications & outreach

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Business Impact

This system helps:

• reduce manual lead qualification time
• respond faster to high-intent prospects
• standardize sales analysis
• personalize outreach at scale
• prevent unsafe or adversarial automation

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⚠️ Limitations

This is a prototype, not a production system.

Missing components:

• persistent storage (DB / CRM sync)
• monitoring & logging
• retry / fallback logic
• advanced prompt injection defense
• A/B testing for email generation

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Next Steps

• API integration (FastAPI / webhook)
• CRM integration (HubSpot, Pipedrive)
• improved scoring calibration
• multilingual support
• analytics (conversion rate, response time)
• stronger adversarial input detection

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Positioning

This project is not a prompt demo. It is a decision system.

It combines:

• LLM-based reasoning
• structured outputs
• deterministic scoring
• safety-aware design
• business workflow automation

The goal is to show how AI can be deployed as a reliable operational layer in real acquisition and sales pipelines.

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📄 License

MIT