Omoshola Owolabi

AI researcher and engineer. I build systems that are trustworthy not just when they are deployed, but over time — in regulated financial environments, critical supply chains, and the infrastructure that moves money between people.

The technical side

I build explainable AI systems — not explainable as a feature bolted on at the end, but as a design constraint that shapes every decision from feature engineering to model selection to how the audit trail gets generated. In practice that means credit risk models that produce adverse action reasons satisfying ECOA and FCRA. Supply chain risk intelligence that a procurement officer can interrogate rather than just trust. Probabilistic planning engines that show their uncertainty instead of hiding it inside a point estimate.

I work across the full stack — from the mathematics of fairness constraints and uncertainty propagation, to the engineering of production inference pipelines, to the regulatory interpretation of what "explainability" actually requires in a given jurisdiction. The research and the implementation inform each other. You cannot do one well without doing the other.

My published research has accumulated over 100 citations, covering ethical frameworks for AI in financial decision-making, machine learning for credit risk prediction, blockchain in supply chain finance, and network analysis for systemic risk assessment. I contribute to IEEE international standards development across AI ethics, cybersecurity, financial LLM requirements, and supply chain security.

The non-technical side

I grew up in Nigeria. My path into this field came through trade finance and supply chain operations, not through a straight line from school to research. I spent years working inside the systems that AI is now trying to improve before I started building the AI. That gave me something a purely academic route would not have: I understand what it feels like to be on the receiving end of a bad model's output.

That background shapes everything. When I write about algorithmic bias in credit scoring, I am not writing abstractly. I have seen what it means for communities where the formal financial system has historically not worked for them. The governance questions are personal before they are professional.

I review research, judge hackathons and statistics competitions, mentor practitioners earlier in their careers, and have submitted technical commentary to federal AI policy processes — because the people who build these systems should be in the room where they get governed, not watching from outside.

Outside the work I think seriously about African knowledge systems and what they have to teach us about computation and intelligence. The connection between Ifa divination's binary structure and the mathematical foundations of computing is not a metaphor. It is history that most people in this field have simply never encountered.

What I am building

Everything I am building right now is pointed at the same question: what does it actually take for an AI system to be trustworthy over time — not just at launch, but as the world changes around it, as decisions accumulate, as edge cases surface, as regulators ask questions? That question has two parts: the domain systems that need to be trustworthy, and the agent infrastructure that makes them run.

Nexus is a supply chain intelligence platform built from scratch in Rust. It treats demand, lead times, and supplier reliability as probability distributions rather than point estimates, runs Monte Carlo simulation across thousands of scenarios, and uses an agentic layer to surface recommendations that a procurement team can actually audit. The hard problem in supply chain planning is not the algorithm. It is making the algorithm's reasoning visible enough that a human expert will act on it.

Zex is a payments platform built around one idea: financial systems should prove what happened, not just assert it. Every transaction execution produces a cryptographic receipt. Finality is decided by a separate truth kernel that evaluates evidence against versioned policy and issues rulings that can be deterministically replayed years later. Reconciliation is a pure function with no side effects. If a regulator asks for the audit trail in five years, the system reconstructs it exactly, from committed evidence, without retrofitting anything.

Alongside those, I am building the infrastructure layer that agents themselves need. Cortex is a web navigation system for AI agents — not a scraper, but a cartographer. It classifies pages, maps site structure, and gives agents the context to move through documentation and web resources the way a researcher would rather than the way a script does. AgenticMemory is a persistent memory substrate for agents: a binary graph format that stores decisions, corrections, and learned facts with semantic edge types so an agent can reconstruct what it knew and when. Both are in active development and both have posts here.

The thread connecting all of it: agents operating in financial and regulated environments cannot just be capable. They need to be auditable, correctable, and honest about what they do not know.

Get in touch

I am open to speaking engagements, research collaborations, peer review, and advisory conversations — particularly around AI governance, explainable AI for financial services, agentic systems infrastructure, and supply chain risk intelligence.

Reach me by email or on LinkedIn. Research output is indexed on ORCID.