About Auditlynk
Founded on a simple observation: regulated industries deserve better AI partners.
Auditlynk is an AI consultancy that designs, builds, and governs machine learning systems for financial services and healthcare organizations.
Our story
Auditlynk was founded in 2026 to help teams close the gap between a promising model and a system their organization can responsibly operate. We focus on financial services and healthcare because the review, safety, and documentation requirements in those fields need dedicated attention.
The practice is built around a familiar problem: promising models can lose momentum when lineage, validation criteria, or ownership are addressed too late. Good technical work still needs a clear evidence trail and a workable governance plan.
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 based in Spain. 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.
Auditlynk is an early-stage practice founded in 2026; we are currently taking on our first engagements.
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
Preston Weekes
Founder & Principal Consultant
Preston founded Auditlynk in 2026 and leads the practice from Spain. He is the primary contact for new engagements and is responsible for project scoping, delivery standards, and the firm's governance-first method.
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.