About — Clavenix Technologies | MLOps & Machine Learning Engineering

Clavenix was founded to solve one specific problem: healthtech and fintech teams with working Deep Learning models that can’t get to production. It’s an engineering problem, not a data science problem — and one that requires hands-on experience in regulated deployment contexts to solve correctly.

The founder has deployed Machine Learning models in live clinical environments, including production medical imaging systems for radiology workflows. Brain tumour detection from CT scans. COVID-positive classification from chest X-rays. Both running reliably in clinical settings where a prediction failure is not a bad dashboard metric — it’s a clinician without the information they need.

We work with Series A and B funded healthtech and fintech startups. Typical client: a CTO whose data science team has models that work in the lab but haven’t made it to production. Typical timeline from first call to deployed model: 4–8 weeks.

We don’t build models. We take models that already work and build the Machine Learning Engineering infrastructure to run them reliably in regulated environments.

Three things specific
to this firm.

Regulated environments only.

HIPAA Technical Safeguards, DICOM data handling, delayed ground truth monitoring, and the compliance implications of a misclassified prediction are not in general MLOps training. We work exclusively in healthcare and fintech.

The deliverable is independence.

Every Machine Learning Engineering engagement ends with a CI/CD pipeline, monitoring setup, and documentation your team can operate without us. We close a capability gap — we don’t open a dependency.

Scope before work. Always.

Engagements are scoped and structured before you commit. The Readiness Audit exists to make that scoping evidence-based rather than estimated. No surprises mid-project.

Deployments, not
slide decks.

2024

Clinical Deep Learning deployment — radiology workflow

Two models deployed to production in a healthcare provider’s radiology workflow. Brain tumour detection (CT) and COVID classification (chest X-ray). DICOM pipeline, HIPAA audit logging, and delayed ground truth monitoring in a live clinical environment.

2025

Clavenix founded

Founded to solve the MLOps and Machine Learning Engineering gap for healthtech and fintech teams — specifically the engineering problem between a working model and a reliably operating production system.

2025

Core MLOps methodology formalised

Readiness Audit, Core Deployment Project, and Complex Multi-Model Project defined as fixed-scope engagements based on patterns and failure modes from early healthcare and fintech deployments.

2026

Expanding fintech Machine Learning Engineering practice

Active engagements in fintech — fraud detection, credit scoring, and transaction anomaly Deep Learning models. Same compliance-first, fixed-scope approach applied to SOC2 and financial regulatory environments.

Built by someone
who’s done
the deployment.

A specialist firm,
not a generalist one.

“Close the gap between a working
Deep Learning model and a system
that operates reliably in production.”

Three things specific
to this firm.

Regulated environments only.

The deliverable is independence.

Scope before work. Always.

Deployments, not
slide decks.

The Readiness Audit
is the right first step.

Built by someonewho’s donethe deployment.

A specialist firm,not a generalist one.

“Close the gap between a workingDeep Learning model and a systemthat operates reliably in production.”

Three things specificto this firm.

Regulated environments only.

The deliverable is independence.

Scope before work. Always.

Deployments, notslide decks.

The Readiness Auditis the right first step.

Built by someone
who’s done
the deployment.

A specialist firm,
not a generalist one.

“Close the gap between a working
Deep Learning model and a system
that operates reliably in production.”

Three things specific
to this firm.

Deployments, not
slide decks.

The Readiness Audit
is the right first step.