Conflicting data from multiple sources
Source provenance, confidence rules and explicit review states.
Lead Engine
A lead discovery and qualification platform that collects public business data, analyses opportunities, scores prospects and creates personalised outreach angles.
- Role
- Product design, AI integration and full-stack engineering
- Selected result
- Automated research-to-outreach workflow
- Technologies
- Next.js / Node.js / PostgreSQL / OpenAI / Automation
03
Manual lead research was slow, inconsistent and difficult to translate into focused outreach. The product needed to turn public signals into explainable prioritisation.
Constraints
- Noisy public business data
- Duplicate and incomplete records
- Explainable AI scoring
- Responsible outreach workflows
- Product workflow design
- Data pipeline architecture
- AI model integration
- Lead management interface
- Automation and deployment
Build a staged enrichment pipeline that preserves sources, confidence and rationale so every score remains reviewable.
04 / Architecture
A product system designed as connected layers.
- 01Discovery
- 02Normalisation
- 03Enrichment
- 04AI qualification
- 05Pipeline
- 06Outreach support
- 01
Preserve source evidence alongside every enriched field.
- 02
Separate deterministic scoring inputs from model-generated analysis.
- 03
Make deduplication a first-class pipeline step.
Opaque qualification decisions
Structured scoring rationales and human-adjustable criteria.
Automated research-to-outreach workflow
Only supplied or clearly verified project information is shown here.