I take research out of the lab, ideas out of notebooks, and AI ambition out of strategy decks — and turn them into systems that work, in domains where governance and getting it right are not optional.
Whether the starting point is a research finding, an idea on a napkin, or an organization's AI ambition, the work is the same: turn possibility into something real and deployable.
For faculty, doctoral students, and research groups whose frameworks, models, or tools have value beyond the journal — and no clear path to a product, venture, or platform.
See the Research →For entrepreneurs, subject-matter experts, and non-technical founders moving from idea to validation to a product people will actually use.
See the Outcomes →For teams and departments adopting AI, automating workflows, or building governance into systems where the cost of getting it wrong is high.
See the Practice →For universities, corporations, and associations wanting a clear, credible perspective on AI, tech trends, and the entrepreneurial ecosystem.
See the Speaking →Most projects fail because of inadequate requirements, flawed design, or insufficient testing — not because the underlying idea or technology was wrong. The work runs in three phases, whoever the client is — and the phases are inseparable.
The decisions made before any code is written or any venture is formed. What is worth building, what is actually required, what evaluation looks like, what the risk profile actually is.
The discipline of turning research, prototypes, and raw ideas into systems and ventures that survive contact with real users — taught and applied.
Whether the output is a research prototype, a startup product, or an enterprise AI system — if the cost of getting it wrong is high, technical and policy questions are inseparable. I work on both.
My doctoral research calls for a paradigm shift in how online misinformation is thought about and verified, arguing that current fact-checking and misinformation detection systems miss harmful content because of how the problem is framed and a structural misspecification of how the systems are designed.
The work formalizes two constructs — Narrative Blindness and the Risk Irrelevance Principle — that explain the misspecification, and demonstrates computationally that a risk-aware alternative is achievable. It also provides a formal definition for Online Health Safety.
This research earned the IEEE ICDH Best Student Paper Award in 2025. The findings have direct implications for platform governance, digital public health, AI content policy, and equitable AI deployment.
Dissertation From Misinformation Detection to Online Health Safety: Narrative Blindness, Risk Irrelevance, and the Limits of Epistemic Conformity
Read the Research →Keynotes, panels, and convenings on applied AI, productization, tech entrepreneurship, trustworthy AI and the policy questions emerging around evaluation and governance.
Topics: Applied AI strategy in high-stakes domains · Deepfake Detection and Trustworthy AI · Productization for founders · AI policy for emerging markets · Tech Entrepreneurship and Startup Coaching · Online Health Information and Misinformation.
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A practitioner's account of why current AI evaluation frameworks fail in high-stakes domains, and what a narrative-aware alternative looks like in practice.
Read the EssayOn the architectural and evaluation decisions that determine whether a system survives contact with real users — and the discipline of teaching this to founders.
Read the NoteA talk on what AI means for the software industry, what careers in software development look like today, and how to position yourself in a rapidly changing field.
Watch the Talk →
Two decades of software development, advisory, and training brought me here today. My career spans the full lifecycle of software operations. I transitioned from supporting application users to looking after e-trading mortgage lending platforms, managing the development of capital markets applications — including AML risk-rating and regulatory compliance infrastructure — and managing the implementation of bank lending and treasury systems before going solo.
As the founder of iBez, I have taken products from concept to deployment for clients across government, healthcare, education, and financial services. I also built Handy-Jacks, a platform of verified tradespeople and trained professionals on software development and digital skills.
Digital and AI strategy disconnected from operating reality is the failure mode I work hardest to avoid. The technical methods matter less than the operations they automate, the pain points they fix, and whether a system is ultimately safe and useful for the people it serves.
Three starting points — a research finding, a founder's idea, a consulting practice — and what each became.
An idea to make it easy for homeowners to find good trade workers became Handy-Jacks, a platform connecting verified tradespeople with the homes and organizations that need them. It launched first in Lagos. A separate pilot in Edo State, backed by DAI/MADE II as a job-creation tool, registered roughly 1,500 trade workers and opened digital markets to artisans who had never used technology to win work.
My doctoral research argues for a paradigm shift in how online health misinformation is assessed — from “is this accurate?” to “is this safe to act on?” It defines Online Health Safety as a distinct problem space and formalizes two new constructs, Narrative Blindness and the Risk Irrelevance Principle. VERITAS, the award-winning neuro-symbolic framework that operationalizes them, reads health posts as whole narratives and grades them into four risk categories instead of a true-or-false label, with direct implications for platform governance, health informatics, and public health policy.
Explore the Research →Founded in 2013, iBez has taken products from concept to deployment across government, education, healthcare, retail, NGOs, and financial services — an electronic medical record, a blood-data system, learning platforms, a fintech platform — in two markets with very different constraints. The practice now helps researchers, founders, and organizations do the same with AI.
Open to research commercialization and productization advisory, applied AI strategy and governance engagements, executive and founder education, startup mentoring and judging, and speaking invitations.
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