MLOps • Machine Learning Engineering • Deep Learning

Production ML
without the wait.

Clavenix operationalises Deep Learning and Machine Learning Engineering for healthcare and fintech teams — from notebooks to HIPAA-grade production on GCP and AWS.

Book a Readiness Audit → See how we work
4–8 wks
Notebook → Prod
HIPAA
By default
Featured field guide

Know your ML maturity.
Choose the next move.

A practical 18-page field guide for engineering leaders who need to turn an ML prototype into a dependable, compliant production capability.

5 maturity stagesHealthcare & fintechFree PDF
Explore the guide
Cover of The 5 Stages of ML Deployment Maturity guide
18pages of
clarity
Why Clavenix

Three things that separate
us from the rest.

01 — SPECIALISATION

Regulated environments. Not general MLOps.

HIPAA Technical Safeguards, DICOM pipelines, delayed ground truth monitoring, and clinical compliance implications are not covered in standard Machine Learning Engineering playbooks. We work exclusively in healthcare and fintech.

02 — OWNERSHIP

Your team operates it. Independently.

Every Deep Learning deployment ends with a CI/CD pipeline, monitoring setup, and documentation your team can run without us. We close capability gaps — not open retainer dependencies.

03 — PREDICTABILITY

Scoped and agreed before work begins.

Deliverable, timeline, and structure are defined before you commit. The Readiness Audit exists to make that scoping evidence-based rather than estimated.

What we do

Three ways
to engage.

Fixed scope. Milestone-based. Every MLOps engagement ends with infrastructure your team owns outright.

01 — First step

MLOps Readiness Audit

We audit your models, infrastructure, and team workflows over 3–5 days. Written report. No obligation to continue.

3–5 daysModel artefact reviewHIPAA gap analysisDeployment roadmap
02 — Most common

Core Deployment Project

Your Deep Learning model is ready. We build the Machine Learning Engineering infrastructure to run it reliably in production.

4–8 weeksCI/CD pipelineDrift monitoring30-day support
03 — Enterprise scale

Complex Multi-Model Project

Multiple Deep Learning models, complex data pipelines, strict compliance. Custom orchestration and full HIPAA or SOC2 architecture.

Timeline variesVertex AI / AirflowAuto retrainingHIPAA / SOC2
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What clients say

Results that
speak plainly.

A
Arjun Mehta
CTO
HealthBridge AI
Our radiology model had been “almost ready” for six months. Clavenix ran the Readiness Audit, identified three blockers we hadn’t seen, and had us in production eight weeks later. The monitoring setup caught two drift events our team would have missed entirely.
P
Priya Nair
VP Engineering
NexCapital Fintech
We evaluated three MLOps firms. Clavenix was the only one who talked about what would go wrong after deployment — not just how they’d get us there. The documentation was good enough to onboard a new engineer with zero involvement from them.
S
Siddharth Rao
Head of AI
MedVision Technologies
The Readiness Audit alone was worth it. It identified a DICOM handling issue we would have discovered in production — in a clinical setting. That report paid for itself before any Machine Learning Engineering work began.
Technical deep learning infrastructure representing Clavenix Machine Learning Engineering work
About Clavenix

Built by someone
who’s done it.

Clavenix is a specialist MLOps and Machine Learning Engineering firm. The founder has deployed Deep Learning models in live clinical environments — brain tumour detection in radiology, COVID classification at clinical load. The work reflects what’s been built, not read about.

Our story →
2
Clinical models deployed
GCP+AWS
Cloud platforms
4–8 wks
Typical timeline
100%
Fixed-scope
Get started

Your models are built.
The infrastructure isn’t.

The Readiness Audit is the fastest way to understand exactly what’s blocking your MLOps deployment — and what it will take to fix it.

Book a Readiness Audit → 20-minute call first

No commitment. We’ll confirm the right fit before you book.