The Signal
Is in the Noise.
Delphi builds the ML infrastructure to find it. A modular prediction and risk intelligence engine — deployable against any domain where signal is buried in exponentially growing data: markets, fraud, policy, and beyond.
The Core Problem
Every high-value domain is a superposition of signal and noise. As data volume grows exponentially, noise compounds faster than signal — until the two become indistinguishable without the right decomposition.
NOISE FLOOR DECOMPOSED · SIGNAL COMPONENTS ISOLATED · UNCERTAINTY QUANTIFIED
The Problem
The Noise Is the Problem.
And It’s Accelerating.
Every domain that matters — markets, fraud, risk, policy — is drowning in data. The volume doubles roughly every two years. Regulatory filings, transaction streams, public disclosures, behavioral signals: the ratio of noise to actionable signal keeps getting worse, not better.
Generic ML models weren’t built for this. They over-fit to noise, miss temporal structure, produce uncalibrated point predictions with no uncertainty estimates, and collapse the moment the data distribution shifts. The infrastructure problem is unsolved — and the cost of getting it wrong is compounding.
What We Build
A Discipline,
Not a Product.
Delphi builds prediction and risk intelligence systems. What transfers across domains isn’t shared code — it’s a rigorous approach to the same hard problem: separating signal from noise in complex data, quantifying uncertainty honestly, and shipping systems that hold up in production.
The Approach
Signal Extraction
Ingests structured data streams in real time, applies NLP embeddings and temporal feature engineering to decompose noise, and surfaces statistically significant forward-looking signal — domain-agnostic.
Calibrated Inference
Ensemble ML models output full predictive distributions, not point estimates. Every prediction ships with confidence intervals, ECE-calibrated probabilities, and Bonferroni-corrected significance. No black boxes.
Model API & Attribution
Inference served via REST and WebSocket at sub-120ms p99. Every response includes SHAP feature attribution — not just what the model predicts, but exactly which inputs drove the output and by how much.
Proof of Work
Predictive Intelligence Research
Applied to publicly available structured data — government disclosures, regulatory filings, institutional event streams. Out-of-sample R² of 0.91 across a 24-month walk-forward validation. The data is public. The edge is in how you read it.
MINDWISE — Production Risk System
A separate, live system built by the same team under the same discipline. Real-time anomaly detection for financial institutions. 2.4B+ inferences/month. 0.03% false positive rate. $850M+ in fraud prevented. Different problem, same rigor.
The Pattern, Not the Instance
Two very different systems. The common thread isn’t architecture — it’s the insistence on calibrated uncertainty, out-of-sample validation, and honest error bounds. That intellectual discipline is what Delphi brings to any high-stakes prediction problem.
Traction
Early Signal. Measurable Results.
Validated across live and backtested regimes. The models perform where it counts — forward.
Team
Built by Operators
Who Don't Guess.
Delphi’s founding team has shipped production ML systems at scale — from real-time fraud detection infrastructure to security intelligence automation — and is applying that engineering depth to applied predictive AI.
ML systems engineer and security researcher. Built and operates the MINDWISE production AI platform — 2.4B+ inferences/month. Previously at NASA, Azorian Cyber Security, Automatiq. Specializes in high-throughput ML pipelines and applied AI at institutional scale.
ML researcher specializing in probabilistic forecasting, causal inference, and NLP for structured financial text. Designed Delphi’s BERT embedding layer and ECE calibration framework. Bayesian ensemble architecture and walk-forward validation methodology.
ML platform engineer. Designed and operates the MINDWISE inference cluster — Kubernetes-orchestrated, 2.4B+ model calls/month, 99.9% uptime. Owns feature store, model serving layer, and CI/CD pipelines for Delphi’s production ML stack.
Why Now
The Noise Problem
Only Gets Worse.
Data volume is growing exponentially. The tools to distinguish signal from that noise are not. Every high-stakes domain — financial, regulatory, operational — is becoming harder to reason about without purpose-built prediction infrastructure. That gap is the market.
Get In Touch
Ready to See What
We've Built?
We're speaking with select accelerators and seed-stage investors. Request the investment deck or schedule a technical deep-dive with the founding team.
Or email directly: invest@delphilabs.io