PDT is the runtime layer that turns your team's recurring operations into governed, versioned, and auditable workflows.
Because prompts aren't processes.
The Governance Gap: Teams at scaling startups are using AI to run experiments, pipeline reviews, and operational analysis — but the process lives in a Notion doc, a Slack thread, and one analyst's memory.
The Result: Every run is different. There's no audit trail, no version history, no repeatability guarantee. And when something goes wrong, nobody knows what actually ran.
Not Model Selection — Execution Shape: Unit token prices have dropped 90%, but AI spend is soaring. The primary cost driver isn't which model you choose — it's how many uncontrolled recursive calls it makes.
Context Rot & Latency: Appending entire conversation histories to every step degrades model accuracy, causes models to lose core instructions, and makes token spend completely unobservable.
Improvisation by Default: A prompt has no version history, no audit trail, no repeatability guarantee. The model reasons differently each run — acceptable for a draft, unacceptable for operations.
The Accountability Gap: When compliance, finance, or leadership asks what your AI did and why, there's no answer. Governed processes replace that gap with a full, step-by-step run trace.
Author workflows in Markdown
Compile to production
Run secure endpoints
Git-Native by Design: Process owners write operational steps in plain Markdown files that live in Git — readable by anyone on the team. Tool scripts integrate with your existing stack. PDT executes them step by step, pausing for human approval at every decision point.
Context Pruning: PDT splits workflows into sequential steps. The model reasons inside a pruned, step-specific context window — eliminating token bloat, context noise, and the degradation that comes from unbounded conversation history.
Execution Bounding: The runtime controls what the model can execute at each step, preventing infinite inference loops and making token spend fully observable and predictable.
Instant Endpoints: Running pdt deploy auto-provisions API webhook endpoints to catch event triggers from your CRM, support tooling, and messaging systems — no infrastructure config required.
Automated Tracing: The hosted platform provides a visual dashboard for step-level audit trails, token cost tracking, and human-in-the-loop approval queues.
Go-to-Market: Bootstrapping with forward-deployed implementation engagements. Target customers: RevOps and Growth ops leads at Series B B2B SaaS companies building operational infrastructure from scratch.
Design Partner Profile: Teams replacing the Notion SOP + Slack thread + ad-hoc prompt combo with governed, Git-native workflows they can run reliably, test, and hand off — without it living in one person's head.
Operations & Product: Co-founder and product head (Followup Edge); Lead Global Operations Analyst managing supply chain and finance metrics (KCI / Acelity).
Systems Engineering: Ex-Senior Analytics Engineer scaling high-volume data platforms and operational pipeline architectures (HubSpot, Cameo).
Contact: christophergdavis@gmail.com | LinkedIn
The Standard: PROCESS.md becomes how operations teams ship governed AI workflows — the way .sql files are how data teams ship transformations. Versioned, testable, and owned by the people closest to the process.
A New Role Emerges: The Workflow Engineer owns the operational layer between AI capabilities and business outcomes — the way the Analytics Engineer owns the data layer. PDT is their tool.
