AI-Driven Lead Scoring & Prioritisation System
Designing a scoring system to prioritise leads based on real behavioural and operational signals.
Context
The system handled a large volume of incoming leads, but teams lacked a structured way to determine which leads were more valuable or time-sensitive.
This resulted in inconsistent prioritisation and inefficiencies in follow-up processes.
The Problem
The goal was to design a scoring system that could combine structured data (fields, activity, metadata) with unstructured inputs (notes, interactions) while keeping results explainable and consistent.
Key challenges: • Balancing automated scoring with transparency • Handling incomplete or inconsistent data • Avoiding over-reliance on AI outputs • Integrating scoring into existing workflows without disruption
System design
I designed a hybrid scoring system where deterministic logic handled core scoring factors, while AI-assisted processing extracted signals from unstructured inputs. • Backend handled scoring calculations and data aggregation • AI layer processed qualitative signals (intent, urgency, context) • Results were normalised into a consistent scoring model • Scores were integrated into the CRM for real-time visibility
- Laravel
- MySQL
- Angular
- API integrations
- Data processing pipelines
- Background jobs
- Laravel
- MySQL
- REST APIs
- ClaudeAI / LLM
- Angular
Outcome
• Clear prioritisation of leads based on actionable signals • Improved response time for high-value opportunities • Reduced reliance on manual judgement • More structured and predictable sales workflow
Reflection
I would introduce a more modular scoring configuration earlier, allowing non-technical adjustments to scoring rules without requiring code changes. This would improve flexibility and allow faster iteration as business needs evolve.
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