Daniel RobertsAI Platform, Technical Program & Product Leader
I build and operate autonomous AI systems, and run the programs that ship them. Currently: a 124-service, dual-GPU research-and-trading platform operated solo through a governed fleet of AI agents. Previously: $3M+ in delivered platform impact across telecom and e-commerce.
What I’m building now.
autonomous markets platform
Autonomous crypto-derivatives research & execution: 117 self-healing services, 3.5 TB proprietary corpus, microsecond execution loops, institutional-grade statistical validation.
Explore →prediction-market intelligence
22-collector data spine, a nine-layer data model, and self-proving strategy governance: gate → shadow → canary → live.
Explore →the earlier acts
Encrypted field tooling, monetized internal platforms, and data-driven ops programs from 25 years in telecom.
Explore →Two live systems, one federated architecture, independent infrastructure sharing data through a strictly read-only seam.
Frictionless, lean, evidence-first.
Measure first
Before I trust a result, I price it against reality, real execution costs, out-of-sample, with the pass bar written down before the data arrives. Most candidates die here, and I record why.
Prove forward
What survives is enrolled forward-only and judged out-of-sample against a frozen verdict rule. The gap between a backtest and a live shadow is where I keep myself honest.
Automate everything
I treat downtime as theft and a human in the loss path as a bug. The systems deploy, monitor, and repair themselves 24/7, and publish their own health.
CPMAI: the discipline every result runs under.
AI projects fail at ~80% industry-wide; CPMAI exists to invert that — a vendor-neutral, iterative, data-centric method in six phases.
Business Understanding
Define the business problem and expected value before any technical work begins, so AI addresses a real need rather than technology for its own sake.
Data Understanding
Explore and assess the data landscape to determine feasibility and surface gaps, before they become costly discoveries later in the lifecycle.
Data Preparation
Transform raw data into AI-ready datasets through cleaning, labeling, and feature engineering, the most time-intensive and performance-critical phase.
Model Development
Build and train models that address the defined objectives, with development kept purposefully aligned to business requirements.
Model Evaluation
Assess performance against business requirements, technical metrics, and ethical standards, with bias and fairness checks, before any deployment.
Model Operationalization
Deploy into production with monitoring, governance, data-drift detection, and continuous improvement.
This isn’t framework name-dropping: the same six phases govern Chimera and Prescient today. Watch them run →