Hunter Jackson
Rough Idea In. Working Product Out.

I took my full business development workflow and automated every step of it, from sector identification to scored, verified target lists ready for outreach. The input is a sentence.

Describe a sector ("mid-market healthcare IT services companies in the Southeast") or paste a single company URL, and the system finds similar companies, researches each one, scores them with PELS (my proprietary exit-likelihood scoring model, explained below), enriches contacts via Apollo, verifies emails, runs ownership QA, and delivers a finished Excel workbook, sorted by exit likelihood.

What used to take an analyst 1–2 weeks of manual research now takes hours, runs autonomously, and produces higher-quality output because the AI never skips a step. Every company gets the same 8-step treatment. Every list gets the same QA validation. The playbooks enforce quality: no shortcuts, no human error.

One sentence in. Scored, verified, ready-to-dial target list out.

The Playbook System Behind It

I built a full library of executable playbooks that turn rough company names into institutional-quality deliverables, automatically. These aren't templates. They're step-by-step AI workflows that enforce quality gates, cross-reference data, and flag risks before a human ever sees the output.

• IB Deal Brief Playbook: Paste a company link or name → 8-step framework runs automatically: intake → deep dive → financials → precedent transactions → public comps → valuation → diligence flags → deal assessment. Generates 20+ page briefs.
• PELS Methodology: 34,000-word scoring system covering 5 dimensions (Personal, Economic, Lifecycle, Strategic, Market). Full rubric for every score from 0–100.
• Target List Playbook: Describe a sector → 8-step pipeline runs: company sourcing → ownership screening → PELS scoring → Apollo enrichment → email verification → QA validation → delivery. Produces verified, scored Excel workbooks.
• Account-Based Origination: Paste a company link → maps decision-makers, builds relationship timelines, and generates personalized outreach sequences.
• RPE Analysis + Benchmarks: Revenue-per-employee models across 7 subsectors with Capital IQ-sourced benchmarks. Estimates revenue when financials aren't public.
• BD Master Plan: 29,000-word strategic plan covering sector prioritization, outreach strategy, competitive positioning, and pipeline management.

Every playbook is version-controlled in git. The AI agent runs them autonomously. Drop a company name and get a finished deliverable by morning.

I needed to find companies likely to sell. PitchBook costs $25K+/year and covers 5% of the market. So I built my own: a 5-dimension, 0–100 scoring model that predicts exit likelihood within 12–24 months, using only free public data.

The key insight I reverse-engineered: the strongest predictor isn't financials. It's the owner's personal lifecycle. No platform tracks this. Mine does.

• Owner Demographics & Lifecycle: Age, tenure, succession gaps, retirement signals
• Business Maturity & Stagnation: Growth plateau, market saturation, operational complexity
• Market & Industry Timing: Sector M&A activity, buyer appetite, multiple trends
• Advisor & Process Signals: Board changes, new hires, consultant engagement, process indicators
• Financial & Structural Readiness: EBITDA margins, clean financials, audit status, deal-readiness

Validated in production: Example — from 1 PELS-prioritized list of 50 cold outreach targets, 6 responded confirming they're actively exploring a sale, a 12% hit rate on cold outreach to founders considering M&A.

Not a chatbot. A coworker. Rudy runs autonomously on OpenClaw with cron jobs from 5 AM to 2 AM: scanning inboxes, generating research documents, building automated target lists with email verification, committing to git, and consolidating its own memory across sessions.

It has a priority engine that scores every available action by impact, effort, and urgency, then executes the highest-scoring action without being told. When I go quiet, it keeps working. When I come back, it catches me up.

Einstein said there isn't enough time in the day, that humans would be far more productive if they didn't need to sleep. Diary of a CEO has some of the most life-changing, first-person insights on the internet, but every episode is 1.5 hours. I built this to solve the time problem, allowing people to absorb those insights in minutes instead of hours. Faster information transfer for people who don't have time to watch every single episode.

925+ AI-generated podcast summary pages, custom newsletter system, subscriber capture, automated email pipeline with 14 editions queued. 1,650+ monthly visitors and growing.

The newsletter infrastructure is 100% custom — subscriber API, welcome automation, bulk sending, unsubscribe handling, CAN-SPAM compliance. I replaced Mailchimp with code I wrote in under an hour. Automated sends go out to 100+ subscribers and climbing. Total monthly cost: $0.

My rule: if I can build it on existing infrastructure in under 4 hours, I never reach for a SaaS tool. Newsletter system? Built it. Dashboards? Built them. REST APIs? Built those too. Email verification? Built it.

I use the right model for the right job: Claude Opus for complex analysis and deal briefs, Codex CLI for code generation, Sonnet for fast iteration, and open-source models like Qwen and Llama when the task calls for it. OpenClaw orchestrates everything, routing tasks to the right muscle automatically.

My production stack runs on Cloudflare Workers + KV + Pages + Gmail SMTP. Zero subscriptions. Zero monthly fees. Full control.