About Us - Ozagho Innovations - Generative AI Expertise

Products

Two products.
One clear mission.

Both built around the same conviction: decision-grade AI requires causal reasoning, not just statistical pattern-matching.

Live

Unified Consensus Ranking Engine

Regime-aware equity intelligence

A deterministic, multi-model equity ranking system that blends SVM, Random Forest, XGBoost, reinforcement learning, and technical signals into a single stable, auditable output. Built for operators who demand clarity and consistency — not a black box.

Consensus screener with multi-model breakdown
Stock detail view with pillar contributions
Portfolio intelligence for your holdings
Watchlists and saved universes
API access for teams and platforms

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Design Partner Program

Causal Research Engine

The reasoning layer markets are missing

A live, expert-validated causal graph of financial market relationships built on a functional causal model architecture. Integrates denoised news encoding, dynamic causal graph construction, and historical price signals to produce movement predictions grounded in causal structure — not correlation. Every link is validated by domain experts and scored by predictive track record.

Live causal graph across 5+ sectors
Regime-conditional edge activation
Expert validation and confidence scoring
Causal attribution for portfolio outcomes
CausalAlert — real-time chain updates

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Why Ozagho Innovations

The reasoning layer incumbents don't have.

Bloomberg retrieves. AlphaSense summarizes. We reason. That's not a feature difference — it's an architectural one.

Causal, not correlational

Our systems map directional cause-and-effect relationships, not statistical co-movement. When conditions change, we tell you which relationships hold and which ones break — before the market tells you.

Regime-aware by design

Market relationships shift across macro regimes. A model trained on 2019 data misfires in 2022. Our systems are built with regime-conditionality as a first principle, not an afterthought.

Expert-validated, not hallucinated

Every causal link in our graph is validated by domain experts and scored by predictive track record. The confidence you see is earned through longitudinal accuracy, not assumed from training data.

Auditable and explainable

Every output traces to a source, a validation, and a regime condition. For investment committees, regulators, and LPs who need to understand the reasoning behind a decision — not just the output.

Research

Built on a decade of
applied AI research.

Our products are grounded in active research across three open problems in applied AI.

Active research

Causal graph construction from financial text

We apply functional causal models with denoised news encoders to automatically extract and update causal relationships from earnings calls, analyst reports, and news corpora — mapping lag-dependent temporal causal links between assets at scale.

Active research

Regime-aware causal model adaptation

Causal relationships that hold in one macro regime break in another. We're building models that detect regime transitions and adapt their causal graph structure accordingly — a form of continual learning specific to financial systems.

Active research

Uncertainty quantification for high-stakes decisions

Knowing what a model doesn't know is as important as what it does. We apply Bayesian inference, Monte Carlo methods, and ensemble calibration to give decision-makers honest confidence intervals, not false precision.

Prior work

Predictive modeling of ICU outcomes (MIMIC II)

Early applied research on predicting patient outcomes and ICU length-of-stay from the MIMIC II clinical database — one of the foundational applications of causal reasoning in high-stakes sequential decision environments.

Prior work

Weather forecasting via Hidden Markov Models

Research on applying HMM-based sequence modeling to atmospheric forecasting — building intuition for regime detection, state transition probabilities, and the limits of statistical models in dynamic systems.

Coming soon

The Financial Causal Atlas v0.1

A publicly released causal graph covering five financial sectors — semiconductors, energy, financials, healthcare, and industrials — with validated directional relationships and regime annotations. Publication target: Q3 2026.

Fig. 1 — CausalStock: functional causal model architecture

Architecture

How our causal models reason about multi-stock dynamics

Our Causal Research Engine is built on a functional causal model that integrates three reasoning streams simultaneously: news representation via a denoised news encoder, historical price signals via a prices encoder, and a dynamic causal graph that captures lag-dependent temporal relationships between assets.

Rather than treating each stock in isolation, the model constructs a causal graph (G₁–G₄) across time steps — mapping which assets causally precede which outcomes, and under what lag conditions. This produces movement predictions grounded in causal structure, not correlation.

Denoised news encoder evaluates correlation, sentiment, importance, impact, and duration via LLM aspects
Causal graph dynamically updated across time steps — not static relationship maps
Prices encoder captures temporal sequences from multiple historical windows
Aggregation function combines news and price representations before final prediction
Outputs multi-stock movement predictions with distribution of causal link existence

Empirical Results

What the causal model
actually reveals.

CausalStock's outputs can be read from two distinct perspectives — the market structure view and the news intelligence view. Together they form a complete picture of why stocks move.

CausalStock results: causal strength vs market cap scatter plots, inter-stock causal heatmap, and LLM news evaluation aspects

Fig. 2 — CausalStock empirical results: (a) causal strength vs. market capitalization, (b) inter-stock causal relationship heatmap, (c) LLM news evaluation aspect scoring

Perspective 01

The Market Structure View

Panels (a) and (b) — causal strength and inter-stock relationships

Panel (a) tells a striking story: causal strength is positively correlated with market capitalization across both UK and Chinese markets. Larger companies don't just have more market influence — they have stronger causal precedence over other assets. This validates a core assumption of the Causal Research Engine: that causal relationships in markets are not random noise, but structured, measurable, and predictable.

Panel (b) reveals the inter-stock causal graph as a heatmap across 18 major equities (XOM, AAPL, JNJ, AMZN, GOOG, TSM, and others). The intensity of each cell represents the strength of the causal link between two stocks. Several findings stand out:

High-intensity clusters around GOOG and AAPL confirm their role as causal hubs — movements in these names systematically precede movements in others
The diagonal crosshair (centered on GOOG/BBL) identifies a regime boundary — stocks above and below it exhibit structurally different causal behavior
TSM shows elevated causal links to semiconductor and tech names, consistent with its role as upstream supply chain for the sector
Sparse off-diagonal cells between unrelated sectors (e.g. MO and JNJ) confirm the model is capturing genuine causal structure, not spurious correlation

Perspective 02

The News Intelligence View

Panel (c) — LLM evaluation aspect scoring

Panel (c) shows how the denoised news encoder evaluates individual news items across five dimensions: Correlation, Sentiment, Importance, Impact, and Duration. This is not keyword extraction or sentiment analysis — it is a structured causal relevance scoring that determines how much weight each news item should carry in the causal graph update.

Three examples illustrate the nuance the model captures:

AAPL — "Apple Likely to Delay 5G iPhone Launch": High correlation (8) and high impact (9) but negative sentiment (-0.7) — the model correctly identifies this as a high-relevance negative signal that should causally downweight AAPL in near-term predictions
TSLA — "Tesla Could Surpass 1 Million Delivery Mark": High correlation (8), positive sentiment (0.7), high impact (9) — a strong positive causal signal. The model surfaces this as a meaningful upward causal driver
GOOL — "3 Tech Stocks for Growth Investors to Buy": All scores zero. The model correctly identifies this as generic financial content with no causal relevance — it carries no signal and is filtered out entirely

This filtering is the critical differentiator. Most NLP-based financial tools treat all news as signal. CausalStock's encoder knows the difference between a headline that causally matters and one that doesn't — and only the former updates the causal graph.

For Investors

The causal layer
markets are missing.

The problem

Financial markets are the most consequential domain where AI misfires play out at scale — where models trained on one regime confidently misfire in another, where correlation masquerades as causation, and where the cost of being confidently wrong is measured in portfolios, not percentages.

The market gap

AlphaSense has built a $500M+ ARR business on document retrieval and summarization. Bloomberg Terminal serves 325,000+ professionals. Neither offers causal graph reasoning. The reasoning layer is the white space.

The moat

Our data flywheel captures expert-validated causal links — a signal type that generic AI cannot replicate without access to the same stream of domain expert judgment. Every analyst interaction makes the graph richer. A competitor starting today faces a multi-year replication challenge on an asset that compounds daily.

$101M

Avg AI budget per large investment firm, 2026

78%

Of investment leaders say AI remains top priority even in downturn

42%

Projected CAGR of Causal AI market through 2034

Incumbent tools with live causal graph reasoning for finance

Request our investor deck

We share our full pitch deck with qualified investors. We'll respond within 48 hours.

Your name

Firm or fund

AI research since 2014

Computational Statistics, Molecular Biology

Advanced Network Analysis

MIMIC II ICU outcome modeling

HMM-based forecasting research

Irvine, California

Company

A letter from
the founder.

I started building AI systems in 2014, during a graduate course on Computational Statistics for Molecular Biology. What struck me then — and still strikes me now — is that the hardest problem in applied AI isn't prediction. It's understanding why a prediction is correct, and when it will stop being correct. My early research on ICU patient outcomes and weather forecasting using Hidden Markov Models taught me that the most dangerous moment in any AI system is when a statistical pattern holds just long enough to be trusted, and then breaks.

That insight is what Ozagho Innovations is built around. Financial markets are the most consequential domain where this plays out at scale — where models trained on one regime confidently misfire in another, where correlation masquerades as causation, and where the cost of being confidently wrong is measured in portfolios. We build AI systems that reason about cause and effect. Our Unified Consensus Ranking Engine gives operators a stable, regime-aware signal they can trust and explain. Our Causal Research Engine maps the directional relationships between macro forces and market outcomes so analysts can ask not just what is happening, but why — and what changes that.

We are a small team with serious research roots and a clear view of where AI in financial intelligence is going. The tools that win the next decade won't be the ones that retrieve the fastest — they'll be the ones that reason the deepest. That's what we're building, and we're looking for partners who share that conviction.

Hero Ozagho

Founder & CEO, Ozagho Innovations

We build AI that explains why markets move — not just what happened.

Two products.
One clear mission.

The reasoning layer incumbents don't have.

Built on a decade of
applied AI research.

How our causal models reason about multi-stock dynamics

What the causal model
actually reveals.

The causal layer
markets are missing.

Request our investor deck

A letter from
the founder.

Ready to reason about markets,
not just search them?

Company

We build AI that explains why markets move — not just what happened.

Two products.One clear mission.

The reasoning layer incumbents don't have.

Built on a decade ofapplied AI research.

How our causal models reason about multi-stock dynamics

What the causal modelactually reveals.

The causal layermarkets are missing.

Request our investor deck

A letter fromthe founder.

Ready to reason about markets,not just search them?

Two products.
One clear mission.

Built on a decade of
applied AI research.

What the causal model
actually reveals.

The causal layer
markets are missing.

A letter from
the founder.

Ready to reason about markets,
not just search them?