Markets
Alpha extracted from disclosure streams, alternative data, and institutional event flow. Calibrated probabilistic forecasts with full predictive distributions — not point estimates.
On the name
Delphi was the temple of Apollo,
the seat of the Oracle.
For a thousand years, Delphi was where antiquity went when it needed a read on the future — the institutional voice for a question no individual could answer alone. Generals, merchants, and city-states all walked the same road for the same reason: a calibrated opinion about what comes next.
We took the name on purpose. Delphi Labs works the modern version of that problem — extracting forward-looking signal from the noise of the present, with the discipline of a system that knows the difference between conviction and certainty, and is willing to tell you which one it has.
γνῶθι σεαυτόν
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 are indistinguishable without the right decomposition.
NOISE FLOOR DECOMPOSED · SIGNAL COMPONENTS ISOLATED · UNCERTAINTY QUANTIFIED
The Problem
The noise problem
only gets worse.
Every domain that matters — markets, fraud, risk, regulatory, supply chain, operations — is drowning in data. Volume doubles roughly every two years. Filings, transactions, telemetry, disclosures, behavioural signals: the ratio of noise to actionable signal keeps getting worse, not better.
And the noise floor is no longer accidental. The slopification of the Internet — generative content poured into the same channels we read from — is actively polluting the upstream. Synthetic press releases, AI-rewritten filings, model-generated commentary, and bot-amplified narratives now sit alongside the real signal in every feed. The cheapest way to win is no longer to add another model; it is to refuse the slop and prove what's left is real.
Off-the-shelf LLMs and generic ML stacks weren't built for this. They over-fit to noise, miss temporal structure, produce uncalibrated point predictions with no honest uncertainty, and collapse the moment the distribution shifts. The predictive infrastructure problem is unsolved — and the cost of getting it wrong compounds.
# delphi · the discipline, not a product
$ delphi apply --domain=<your-problem>
Engaging the pipeline...
✓ Decompose noise floor
✓ Identify temporal structure
✓ Filter spurious correlations
✓ Monitor distribution shift
✓ Calibrate predictive uncertainty
✓ Attribute every output (SHAP)
discipline = portable
verticals = MINDWISE · GetEven · …
numbers = live on each venture site
✓ Signal ready · uncertainty quantified
The Platform
One stack.
Every prediction problem.
Delphi Labs builds applied AI for predictive intelligence — a single, modular stack for separating signal from noise, quantifying uncertainty honestly, and shipping inference that survives contact with production.
The Discipline
Signal Extraction
Ingest structured and unstructured streams in real time. NLP embeddings, temporal feature engineering, and noise decomposition surface statistically significant forward-looking signal — domain-agnostic by construction.
Calibrated Inference
Ensemble models output full predictive distributions, not point estimates. Every prediction ships with confidence intervals, well-calibrated probabilities, and corrected significance — honest uncertainty, no black boxes.
Attributed API
Inference served via REST and WebSocket. Every response includes SHAP feature attribution — not just what the model predicts, but exactly which inputs drove the output and by how much.
The discipline is portable. The numbers it produces are not — they belong to each domain it's pointed at. See those numbers on the individual venture sites below.
Use Cases
Predictive intelligence,
deployed.
The same stack, tuned per domain. Each engagement ships an inference API, attribution layer, and calibration report on top of your data. Start with one vertical; the rest compose.
Fraud & Breach
Real-time anomaly detection on transactional and behavioural streams. Pinpoint the point of compromise, surface the features that drove each flag, and route the signal where it needs to go.
Risk
Credit, counterparty, operational. Honest uncertainty quantification, distribution-shift monitoring, and calibrated probabilities so risk teams can act on numbers they can defend.
Regulatory
Filings, rule changes, enforcement actions. Embeddings and temporal models surface emerging exposure before it becomes a headline. Built for compliance, audit, and policy teams.
Supply Chain
Demand, lead time, and disruption forecasting. Multi-horizon predictions with confidence intervals routed straight into S&OP, inventory, and procurement systems.
Custom Domains
Energy, healthcare, insurance, defense, climate. The stack is domain-agnostic by design. Bring the data and the question — we attach the feature pipeline, model ensemble, and inference API.
Ventures
The umbrella.
The verticals.
Delphi Labs is the parent: the discipline, the team, the platform. Each vertical applies it to a specific domain and carries its own numbers, its own customers, and its own site.
MINDWISE
Security & Risk Intelligence · Est. 2016
The first vertical. Production ML and dark-web intelligence for security and risk — built and operated since 2016. Credited by Krebs On Security for attributing a carder-marketplace dump to the Jason's Deli point-of-sale breach via ZIP-code statistical correlation, proving the discipline works under real adversarial pressure.
Numbers, customers, and current deployments live on the MINDWISE site.
GetEven
Next vertical · In development
The next application of the discipline. Launching soon. Predictive intelligence pointed at a new domain — same calibration, attribution, and uncertainty discipline as every Delphi venture.
Details, beta access, and metrics will live on the GetEven site at launch.
The Lab · Side Quests
We're a small, bright, curious, ridiculously motivated team that genuinely loves hard problems — the harder, the better. The two ventures above pay the bills. The work below is what we do for the joy of it: side quests where we point the Delphi stack at a domain we have no business being in, just to see what it learns. Sometimes a side quest graduates into a vertical. Most of the time it teaches us where the discipline still bends. Both are wins.
Cassandra
Regulatory enforcement · Research preview
Predicts which public companies are about to receive an SEC or DOJ enforcement action — before the wire. Reads drift in disclosure language across consecutive 10-Q filings, the cadence of SEC comment-letter back-and-forth, modifications to executive 10b5-1 plans, and outside-counsel engagement signals from public dockets. Output is a tiered conviction score, not a stock recommendation.
Open research line. No public product yet. Calibration ledger available on request under NDA.
Sibyl
Pre-pop cultural signal · Research preview
Predicts which emerging artists, products, or ideas will cross from niche to mainstream pop within 90 days. Fuses creator-graph virality, pre-save and pre-order velocity, subreddit-spillover rate into adjacent communities, and the slope of branded search volume. Built as a stress test of how the Delphi stack performs on noisy, fast-decaying, socially-mediated data.
Open research line. Datasets are messy on purpose — the point is to expose where the calibration discipline breaks.
Team
Four operators.
One discipline.
Delphi Labs is a small team by design: an engineer, a markets mind, a product translator, and the human on the other end of the line. Every prediction the platform ships has all four of them in it.
Nico
Chief Technology Officer
The brains of the operation. Creator of MINDWISE — credited by Krebs On Security for attributing a carder-marketplace dump to the Jason’s Deli point-of-sale breach via ZIP-code statistical correlation across dark-web data. Owns the engineering spine that every Delphi venture runs on.
Founder’s voice and long-form story: mindwise.io ↗
Adelaide
Founder · CEO
The financial mind of the team. Conceived GetEven — the pre-disclosure congressional-trade signal product — and owns the market thesis, the signal taxonomy, and the discipline around what counts as actionable alpha vs. marketing arithmetic.
Dmitri
Chief Product Officer
The translator. Aligns engineering rigor with financial conviction so that what ships is both a pleasurable product to use and a rigorously tested, continuously improved system. Owns the product surface across every Delphi venture.
Nadav
Support · Operations
The human surface of the company. Owns customer support, onboarding, and the operational loop that turns a model output into a useful answer for the person on the other end.
Portraits are AI-generated. The people, the roles, and the work are not.
Why Now
Data volume is exponential.
Forecasting tools are not.
Every high-stakes domain — financial, regulatory, operational, physical — is becoming harder to reason about without purpose-built prediction infrastructure. Off-the-shelf LLMs paper over the gap; they don't close it. That gap is the market.
Data Layer
Disclosures · filings · transactions · telemetry · real-time event
feeds · bring-your-own
Feature Engineering
BERT embeddings · temporal lag features · event sequencing ·
drift-aware normalization
ML Models
XGBoost + Bayesian layers · ensemble calibration · 24-fold
walk-forward OOS validation
Inference & API
Kubernetes serving · <120ms p99 · REST + WebSocket · SHAP
attribution output
Δ
Applied AI / Infra
◆
Alternative Data
▲
Fintech & Risk
⬡
Compliance & RegTech
◎
Supply Chain AI
Request the Deck
Ready to see
what we've built?
We're speaking with select accelerators, seed-stage investors, and design partners across our six core domains. Request the investment deck or schedule a technical deep-dive with the founding team.
Or email directly: [email protected]