MLOps Services

Getting to 92% accuracy
is the easy part.
We handle what comes after.

Machine Learning Engineering, Deep Learning deployment, HIPAA-compliant inference architecture. GCP and AWS. Fixed scope. Milestone-based.

Who this is for

If this is your situation,
this page is for you.

You are a CTO or VP Engineering at a Series A or B funded healthcare or fintech company. Your data science team has one or more Deep Learning models that perform well in testing. They have been “almost ready for production” for longer than you’d like to admit.

Most data science teams are built to train models, not to operate them. The gap between a working model and a production deployment is a Machine Learning Engineering problem — and it’s usually not one your current team was hired to solve.

You are deciding between hiring a full-time MLOps engineer, training your existing team, or using a specialist firm for a scoped engagement. If you are in that situation — this is for you.
Three engagements

Fixed scope.
Milestone-based.

01 — Recommended first step

MLOps Readiness Audit

The right starting point if you don’t yet have a clear picture of where your Machine Learning deployment is blocked. Over 3–5 days, we audit your existing Deep Learning models, infrastructure, and team workflows. No obligation to continue after the audit — the report stands on its own.

Book an Audit →
What’s included
3–5 days engagement Model artefact & versioning review GCP / AWS infrastructure assessment HIPAA compliance gap analysis CI/CD pipeline review Written deployment roadmap
02 — Most common engagement

Core Deployment Project

Your Deep Learning model is ready and you need it in production with the Machine Learning Engineering infrastructure to keep it there. Fixed scope. Milestone-based. Ends with a deployed model, a CI/CD pipeline your team can operate, monitoring with defined thresholds, and documentation that doesn’t require us to be present.

Discuss your project →
Typical scope
4–8 weeks 1–2 models end-to-end on GCP or AWS Docker + Artifact Registry CI/CD: train → validate → stage → deploy Data drift & model drift monitoring HIPAA-compliant inference architecture 30-day post-deployment support
03 — Enterprise scale

Complex Multi-Model Project

The right engagement for teams deploying multiple Deep Learning models, handling complex data pipelines, or operating under strict compliance requirements. Custom pipeline orchestration, advanced monitoring, automated retraining, and full HIPAA or SOC2-grade compliance architecture.

Discuss your project →
Typical scope
Timeline varies by complexity Multiple models & shared infrastructure Vertex AI Pipelines or Apache Airflow Automated retraining triggers HIPAA or SOC2-grade compliance Team training & handover documentation
Not ready to talk yet?

Start with a free tool instead.

Two self-serve tools built from the same patterns we find in every Readiness Audit. No email required to get a result.

Free Tool • ~3 min

HIPAA Compliance Gap Checker

21-point check across the 5 HIPAA Technical Safeguard categories, specific to ML inference. Instant gap score.

Check your score →
Free Tool • ~90 sec

Deployment Timeline Estimator

5 questions about your models, cloud, and compliance needs. Get an estimated timeline and which engagement fits.

Get your estimate →
Process

How a typical
engagement works.

01
Week 1

Scoping call + Audit

30-minute call to understand your models, infrastructure, and goals. If the Readiness Audit is the right starting point, we begin immediately.

02
Week 2

Readiness Report

Written report: model and infrastructure assessment, gap analysis, MLOps deployment roadmap, and timeline estimate. Yours to keep regardless of next steps.

03
Weeks 3–8

Deployment Project

Fixed-scope Machine Learning Engineering project. Weekly progress updates. Ends when the Deep Learning model is in production with CI/CD, monitoring, and documentation.

04
Day 30+

Stabilisation & Handover

30-day availability for questions and issues. After this, you operate independently or continue on a maintenance retainer.

FAQ

Before you
reach out.

We work on both GCP and AWS. If you are on a different cloud provider, contact us — we can discuss feasibility on a case-by-case basis.

The Readiness Audit will surface this. If there are model-level issues preventing deployment, the report will identify them with specific recommendations for your data science team to address before Machine Learning Engineering infrastructure work begins.

Yes. In most MLOps engagements, the data science team remains actively involved — particularly in model validation, monitoring threshold definition, and retraining logic. The handover documentation is written specifically for them to operate the infrastructure independently.

We implement HIPAA Technical Safeguards as they apply to ML inference pipelines: access controls, audit logging (input hash, not raw PHI), transmission security (mTLS), integrity controls, and private endpoint design. We are a Machine Learning Engineering firm, not a compliance consultancy. We implement technical controls; you are responsible for organisational and physical safeguards.

That is the point. The Audit gives you a clear picture before you commit to a larger engagement. If the gaps are larger than anticipated, the report will tell you exactly what addressing them requires — with enough specificity to get a second opinion or act on it independently.

The Readiness Audit produces the information needed to scope a fixed MLOps project accurately. We do not provide fixed project scope before assessing what we haven’t yet seen — that would require us to guess at what the Audit is designed to surface.

Ongoing Monitoring & Maintenance Retainer
For teams with deployed models in production. Covers MLOps monitoring, drift alerts, incident response, and minor pipeline updates. Available from Year 2.
Contact us →
Ready?

Your models are built.
The infrastructure isn’t.

Start with the Readiness Audit. 3–5 days. A written report you keep regardless of what happens next.

Book a Readiness Audit → 20-minute call first

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