Jared Burke

• 61% — Reduction in Sales Cycle Length
• **1+ Million ** — Data Points Processed Daily

Sales teams were operating without a reliable view of which opportunities to prioritize. Pipeline reviews were manual, forecasting was inconsistent, and representatives were spending significant time on research that should have been automated. There was no system connecting data signals to revenue decisions in real time.

I built an end-to-end revenue intelligence platform using Python, n8n, and the Anthropic API. The system ingests and processes over one million data points daily from multiple sources, runs predictive analysis against historical win patterns, and surfaces ranked, actionable opportunity intelligence directly to reps. The architecture was built for async processing to handle volume without latency.

We reduced the sales cycle by 63%. Manual pipeline reviews were replaced with data-driven signals. Representatives gained a clear, ranked daily focus without any manual analysis.

• 94% — Reduction in Matching Time
• 10x — Increase in Processing Capacity

Matching startups with investors required four hours of manual analysis per startup. The team could evaluate only 10 – 12 per week, resulting in delayed introductions and inconsistent match quality, which prevented the platform from scaling.

I built a multidimensional matching algorithm that scores across stage, geography, check size, sector, and portfolio fit. I automated the solution with Clay and n8n, from intake and enrichment through to ranked recommendations delivered in minutes.

We reduced the matching time from four hours to 15 minutes. We increase capacity from 12 to 150+ startups per week. Same-day introductions replaced multi-day delays, and we improved match quality through a continuous outcome feedback loop.

• 43% — Conversion Rate Increase
• £800,000+ — Annual Revenue Unlocked
• 67% — Increase in Enterprise ACV

Flat pricing meant enterprise operators got exceptional value at mid-market rates, while smaller operators found the price prohibitive. Win rate on sub-£50,000 deals sat at 23%. Manual pricing was inconsistent and had no predictive logic.

I built an 8-tier dynamic pricing model using automated enrichment of firmographic, usage, and behavioral signals. The predictive model calculated the optimal price per segment based on historical win rates and LTV. I integrated the solution directly into the sales workflow.

Conversion rose by 43%, enterprise ACV grew by 67%, and win rate on sub-£50,000 deals climbed from 23% to 41%, unlocking £800,000+ in year one. The sales cycle was shortened by 28%, and the client went on to raise a seven-figure seed round in 2024.