CI/CD for models
Automated testing, packaging and deployment for models and prompts, with safe rollbacks.
MLOps & AI Engineering
MLOps that keeps AI reliable after launch day.
We set up CI/CD for models, feature stores, monitoring, evaluation and cost controls, so your AI systems stay reliable, auditable and affordable long after the first release.
The problem
Why this work usually stalls.
01
Models that degrade quietly
Data changes, accuracy drops and nobody notices until a customer or a regulator does.
02
Releases by hand
Every model update is a manual, risky process, so updates happen rarely or not at all.
03
LLM costs that creep up
Token bills grow month by month with no view of which feature or team is driving them.
04
No audit trail
Nobody can say which model version made a decision, on which data, with which settings.
What we deliver
MLOps & AI Engineering, built for production.
Pick one piece or the whole programme. Each is scoped to a business metric and shipped, not left as a proof of concept.
Monitoring and drift detection
Dashboards and alerts for accuracy, data drift, latency and errors in production.
LLMOps and evaluation
Prompt and model versioning, automated evaluations on every change and tracing for every request.
Feature stores and training pipelines
Reproducible training on governed features, so models can be rebuilt and audited.
Cost control
Visibility of cost per model, feature and team, plus caching, routing and right-sizing to bring it down.
Model governance
Model cards, approvals and audit trails that satisfy risk, compliance and internal audit.
How we deliver
From first call to production.
Every phase ends with something you can use, not just something you can read.
Always included
- A senior squad of two to four people, led by an engineer from companies like Amazon, Google or Microsoft
- Weekly demos and one business metric agreed upfront
- Founder review every week and sign-off before production
- All code, models and documentation owned by you
Typical tools
01
Weeks 1 to 2
Assess
We review how models are built, released and watched today.
- Inventory of models, prompts and pipelines
- Release and monitoring gaps
- Current cost and reliability baseline
02
Weeks 2 to 3
Design
A platform that fits your cloud and your team.
- Tooling choices with trade-offs
- Release, approval and rollback process
- Monitoring and evaluation plan
03
Weeks 3 to 8
Build
The pipeline goes live on your most important models first.
- CI/CD and monitoring for priority models
- Evaluation on every change
- Cost tracking by team and feature
04
Ongoing
Operate
Run it with your team, or hand it to us.
- Runbooks and on-call for model issues
- Monthly cost and reliability review
- Optional managed MLOps support
Where we apply it
Industries we know well.
Further reading
From the blog.
Why AI Projects Fail Between Pilot and Production, and How to Fix It
Most AI pilots stall before production for predictable reasons. Here is how to spot them early and design pilots that are built to ship.
RAG vs Fine Tuning: How to Choose for Enterprise GenAI
RAG gives a model the right knowledge; fine-tuning changes how it behaves. A clear comparison and decision guide for choosing between them, or using both.
Managed DevOps Services vs an In-House Team: How to Choose
A practical comparison of managed DevOps services and in-house teams, covering cost drivers, coverage, control and the hybrid model most growing companies end up with.
FAQ
Questions we hear a lot.
What is the difference between MLOps and LLMOps?
MLOps covers the build, release and monitoring of machine learning models. LLMOps adds what large language model systems need: prompt and model versioning, evaluation of free-text output, tracing of multi-step agents and close tracking of token costs.
Do we need MLOps if we only have a few models?
If those models drive real decisions, yes, at least the basics: versioning, automated release, monitoring and an audit trail. We size the setup to the number of models you run.
Which tools do you use?
We are platform agnostic and usually build on what you already run, with tools such as MLflow, Weights & Biases, Kubernetes and your cloud's native services.
Can you run our models in production for us?
Yes. Our managed services include running data pipelines and ML models in production, with monitoring for data quality, drift and model performance.
Other services
Everything between ambition and production.
GenAI & AI Agents
Copilots, retrieval systems and agents wired into your real tools and data.
AI Strategy & Roadmaps
Find where AI moves your numbers and sequence a roadmap people believe in.
Data Platforms & Engineering
Lakehouses, clean pipelines and governed metrics that every model depends on.
Analytics & Decision Intelligence
Forecasting, pricing, churn and demand models, plus dashboards people open.
Digital Transformation & AI Governance
Process redesign, change and responsible AI aligned with the DPDP Act and EU AI Act.
Managed Services
Cloud, DevOps, QA and release management, run 24x7 against clear SLAs.
Talk to a founder about MLOps.
Tell us where you are. We will come back within one business day with a point of view, not a sales deck.