AI-powered investor research & enrichment pipeline

Adamant Ventures · Venture Firm · 2024–Present

50% Less manual research

40% Faster data collection

24/7 Autonomous monitoring

Investor DBs, news, CRMs

Multi-step workflow

Summarize, score, auto-research

Enriched prospect hub

Pipeline & follow-ups

The business development team was manually researching investor prospects one by one — pulling data from multiple databases, scanning news sources, cross-referencing CRM records, and compiling notes into spreadsheets.

There was no standardized qualification process, no scoring system, and no way to prioritize which prospects to pursue first. Each prospect took hours of manual effort before anyone could even begin outreach, which meant the team was bottlenecked at the research stage and couldn't scale deal flow without adding headcount.

I mapped out the full research-to-outreach pipeline and identified every manual step that could be automated. From there, I designed a multi-step n8n workflow that pulls raw prospect data from investor databases, cross-references it with CRM records, and sends it to the OpenAI API for automated summarization, qualification scoring, and categorization.

Enriched records are piped into Airtable as a centralized prospect hub, where the team can filter, sort, and prioritize leads based on AI-generated scores instead of gut feel.

I then deployed OpenClaw as an autonomous agent layer that runs scheduled research tasks around the clock — monitoring for new data, triggering enrichment workflows when new prospects appear, and coordinating updates across tools without any manual intervention.

The final enriched data feeds downstream into GoHighLevel, where I built pipeline stages, automated follow-up sequences, and reporting dashboards so the BD team can move straight from qualified prospect to outreach without touching a spreadsheet.