Peter Ryther · AI Operator || Philomath || GTM Hunter || Technophile · New York, NY || Naples, FL
I turn enterprise-sales problems into inspectable AI-assisted workflows: customer follow-ups, pilot plans, value models, and account preparation.
My background spans enterprise sales at Orum, Outreach and Ironclad. This portfolio shows how I connect commercial judgment with practical AI tools—making sources, assumptions and human review visible.
I'm interested in Enterprise and Strategic Account Executive opportunities at growing AI and robotics companies. Explore a five-minute work sample or connect on LinkedIn.
Explore five commercial decisions and work samples →
| Work sample | What it demonstrates |
|---|---|
| Deal Evidence Copilot | Discovery, source-linked follow-ups, and honest commitments |
| Value Case Lab | Capacity versus cash and inspectable economics |
| Pilot-to-Expansion | Qualification, delivery readiness, and expansion judgment |
| Customer Proof Sprint | Customer storytelling and defensible claims |
| Buyer Value Framework | Buyer outcomes, fair differentiation, and useful discovery |
Each includes reusable assistant instructions, a fictional worked example, evaluation cases, and an application build plan. The toolkit includes 16 passing deterministic code tests and a portable five-mode assistant configuration. Text references are authored examples; live model benchmarking and user trials remain planned. These work samples demonstrate an approach, not measured customer results.
Explore the enterprise GTM & AI workflow portfolio →
Three inspectable examples: a fictional customer-proof story with a source ledger and calculation checks; a pilot-to-expansion playbook; and a reproducible Python service scenario model with six tests. These AI-assisted work samples distinguish evidence, assumptions, and simulated results. They are not presented as customer deployments or measured commercial outcomes.
| Project | What It Is |
|---|---|
| salesrobots-gtm | Full go-to-market plan: ICP, pricing, outreach sequences, 60-day launch roadmap |
| cold-email-deliverability-playbook | SPF/DKIM/DMARC setup, 21-day warmup schedule, domain health monitoring protocol |
| ai-outbound-stack | The full Apollo → Clay → Instantly → Inbox system, documented and diagrammed |
| launch-command-center | Interactive sprint tracker dashboard — built with vanilla JS, deployed on Vercel |
- Start with a real decision — define the user, the problem and what a useful output must help them do.
- Make evidence inspectable — separate source facts, assumptions, proposals and measured outcomes.
- Build with AI, review with judgment — use AI for implementation and drafting, then check calculations, claims and usability.
I hold ~50 AI-era domains including futurebrain.io, worldmachines.io, vcworld.ai, and others at the intersection of AI, finance, and the next decade of software. Building selectively — salesrobots.com is first.
Working on enterprise sales, value engineering, or practical AI adoption?