Deploying AI-driven business workflows with the simplicity of a web application.
The missing operational layer for scaling AI in production safely and cost-effectively.
Limitless Execution: Large language models can write code, analyze logs, query databases, and draft complex communications at near-zero latency.
The Challenge: Because these models are stateless and probabilistic, scaling them inside core business operations requires an environment built around control.
The Token Multiplication Trap: Unit token prices have dropped 90%, but enterprise spend is soaring. Uncontrolled agent loops execute dozens of recursive LLM calls behind the scenes.
Context Rot & Latency: Appending entire conversation histories to every step degrades model accuracy, causes models to forget core instructions, and spikes operational costs.
Unpredictable Improvisation: Models naturally optimize for completion. In core operations, compliance requires the exact opposite: halting, documenting, and routing exceptions.
The Pilot Gap: 88% of enterprise AI agent pilots fail to reach production due to this lack of deterministic guardrails, safety boundaries, and cost predictability.
Write workflows in Markdown
Compile to production
Run secure endpoints
Executable Procedures: Process owners author operational steps in Git-native Markdown. Custom utilities are integrated as isolated, single-purpose code scripts.
Context Pruning: PDT splits workflows into sequential steps. The model reasons inside a pruned, step-specific context window, eliminating token bloat and context noise.
Execution Bounding: The runtime controls what the model can execute at each step, preventing infinite inference loops and making token spend fully observable.
Instant Endpoints: Running `pdt deploy` auto-provisions API webhook endpoints to catch event pings from CRM, support, and messaging systems.
Automated Tracing: The hosted platform provides a visual dashboard for step-level audit trails, token cost tracking, and human-in-the-loop approvals.
Current Phase: Pre-product, pre-revenue. The core PDT CLI runtime is under active development.
Market Validation: Operations and product leads reveal a critical gap: they have no mechanism to audit step runs, restrict API writes, or enforce token budgets on autonomous agents.
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: Replacing uncontrolled, expensive agentic loops with versioned, step-isolated, and code-controlled processes in Git.
Goal: Enabling organizations to safely scale AI-driven operations with predictable costs, absolute control, and audit-ready traces.
