About Solenkn
Founded on a simple observation: regulated industries deserve better AI partners.
Solenkn is an AI consultancy that designs, builds, and governs machine learning systems for financial services and healthcare organizations.
Our story
Solenkn was founded in 2026, at a moment when two things were true at once. AI capability had never been more accessible — and the regulatory scrutiny on AI in banking, insurance, and healthcare had never been more intense. Most consultancies were built for the first fact. Almost none were built for the second.
We had watched the same pattern repeat across the industry: a promising model gets built fast, demos well, and then stalls for months in validation because nobody documented the data lineage, nobody tested for disparate impact, and nobody could explain the feature set to a model risk committee. The technical work was fine. The governance work simply never happened.
So we built a firm around the opposite premise. Start with the regulatory requirements. Treat auditability as an engineering constraint, like latency or accuracy. Serve two industries deeply instead of ten industries superficially. And structure every engagement around a five-stage model — Frame, Architect, Build, Deploy, Govern — that produces a continuous chain of evidence from the first workshop to years into production.
We are a deliberately small, senior, remote-first team. The person who scopes your engagement is the person who builds it. That is not a limitation of our size; it is the point of it.
Our mission
To make production machine learning genuinely trustworthy in the industries where trust is non-negotiable. Not trustworthy as a marketing adjective — trustworthy in the specific, verifiable sense that a validator, an auditor, or a clinician can examine the system and understand exactly what it does, why it does it, and what happens when it is wrong.
Founder
Daniel Okonkwo
Founder & Principal Consultant
Daniel founded Solenkn in 2026 after a decade spent building and validating machine learning systems inside regulated environments — first in credit risk modeling at a retail bank, later leading data science delivery for healthcare analytics programs. He has sat on both sides of the model validation table, which shaped Solenkn's core conviction: the fastest way through governance is to build for it from the start. He works directly on client engagements and leads the firm's governance methodology.
Our approach
Governance-first, in practice
Governance before glamour
A model that performs brilliantly but cannot survive a validation review is a liability, not an asset. We optimize for the whole lifecycle — including the parts that happen in front of an examiner.
Evidence as a first-class output
Documentation is not something we write at the end. Data lineage, design decisions, validation results, and monitoring plans accumulate as the work happens, so the audit file is a by-product of good engineering.
Reproducibility everywhere
Every training run can be re-executed. Every dataset is versioned. If a regulator asks why a model made a decision eighteen months ago, we can reconstruct the exact conditions that produced it.
Humans stay in the loop
In credit decisions and clinical settings, models advise — people decide. We design escalation thresholds, override mechanisms, and review workflows into every system where the stakes demand them.
Ready to build AI your regulator can live with?
Tell us about the model you need, the rules you operate under, and the deadline you are working against. We will tell you honestly whether we can help.