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Automating Lead Qualification: AI-Powered Workflow Integration

Ilya Bolkhovsky
Ilya Bolkhovsky -> April 15, 2025
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A digital agency struggling with manual lead qualification sought to optimize their prospecting process. Their team spent 20+ hours monthly sifting through low-quality leads, delaying client acquisition and straining resources.

Our team collaborated with the agency to design an end-to-end automation system using Make.com, Notion, OpenAI, and Telegram Bot API. The solution combined AI-driven lead scoring with human-in-the-loop validation, enabling the agency to prioritize high-intent prospects while maintaining strategic oversight.

Objective

The cross-functional team aimed to:

1. Reduce manual workload by automating ICP alignment checks.
2. Improve lead quality through dynamic AI evaluation trained on sales team expertise.
3. Create feedback loops to iteratively refine qualification criteria.
4. Integrate seamlessly with existing tools (Notion, Telegram) for adoption.

Implementation

__Collaborative System Architecture__ The engineering, AI, and operations teams worked in tandem to: __1. Design ICP-Centric Notion Database:__ – Built fields mirroring sales team’s qualification criteria (industry, revenue, funding stage). – Added Reasoning Log to capture AI/human decision patterns for model training. – Pre-populated with 2,000+ historical leads for baseline analysis. __2. Develop Hybrid AI-Human Workflow:__ – Engineers built Make.com pipelines to process leads through GPT-4o-mini evaluations. – AI specialists designed a RAG-style prompt system using sales team’s playbooks. – Operations configured automated Telegram alerts with approval buttons for team reviews. __Team-Driven AI Training__ To ensure accurate lead scoring: __1. Content Extraction:__ Data engineers processed 180+ past qualification decisions from sales call transcripts. __2. Prompt Engineering:__ AI team built context-aware prompts combining: – Static ICP guidelines from leadership – Dynamic insights from sales team’s historical feedback __3. Model Fine-Tuning:__ Deployed GPT-4o-mini with temperature 0.3 to balance creativity/consistency. __Cross-Functional Integration__ The DevOps and UX teams streamlined adoption through: __1. Telegram Bot Interface (“Sandra”):__ – Added emoji-based approvals (👍/👎) to simplify team feedback. – Programmed personality traits to reduce tool fatigue. __2. Auto-Update Mechanisms:__ – Feedback from sales reviews automatically enriched the AI’s knowledge base. – Nightly syncs between Notion and Make.com ensured data consistency.

Results & Impact

- 80% Reduction in manual lead screening (22 hrs → 4.5 hrs monthly). - 35% Higher Conversion on AI-qualified leads vs manual process. - Self-Optimizing Workflow: Sandra’s accuracy improved by 27% in 60 days through team feedback. - Scalable Foundation: System adapted to 3 new verticals within 6 weeks post-launch. This cross-departmental effort transformed lead qualification from a fragmented task into a cohesive AI-human partnership. By combining engineering rigor with frontline sales insights, we created a system that learns as the team grows—turning prospecting into a strategic advantage.

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