AI Transformation · AI Product Engineering
MLOps & AI Operations
AI systems change after launch: data drifts, providers update models and usage grows. We release, monitor, retrain and support your models and generative AI systems, and report their quality and cost to the people who own them.
Example: AI in production
Live
Demand forecastMachine learning, version 14
HealthyForecast error
Support assistantGenerative AI, prompt version 9
HealthyFaithfulness
Credit risk modelMachine learning, version 6
RetrainingData drift
Drift crossed its threshold. Retraining has started; the new version is released only if it passes the evaluation gate.
How to get started
Three steps to a plan for MLOps & AI Operations
- 1Tell us what you needUse the project form or book a call. A few sentences about the goal is enough to start.
- 2Free technical consultationWe go through your goals, users, existing systems and constraints with you.
- 3Your planA detailed plan covering the right tech stack, architecture, timeline and budget. Then you decide.
Why it matters
Models get worse without anyone changing them
The world a model learned from moves on: customers, prices and behaviour change, and live data drifts away from the training data. Accuracy falls quietly until the model is retrained on recent data.
What we run
Operations for every AI system you depend on
For machine learning models and generative AI systems alike, whether we built them or not.
Releases
CI/CD pipelines for models and prompts, with tests, approvals, canary or shadow releases, and one-step rollback.
Monitoring
Quality, drift, data quality, latency, errors and cost, with alerts routed to named owners.
Retraining
On a schedule or when a threshold is crossed. A new version is released only if it beats the current one on the evaluation set.
Generative AI operations
Prompt and model versions tracked, evaluations rerun on every change, and provider model updates tested before adoption.
Incident response
Runbooks for known failures, response times agreed for each system, and a root-cause review after every incident.
Cost management
Usage and spend by team and feature, with limits, caching and infrastructure sized to the load.
MLOps check
Where are your operations today?
Answer four questions for the models you run. The levels are those of Microsoft's MLOps maturity model.
- Is model training code in version control, with automated builds?
- Is training automated, with experiments tracked in one place?
- Are models released through a CI/CD pipeline with tests?
- Do production signals, such as drift, trigger retraining automatically?
Your level
Level 2: Automated training
Training is automated, tracked and reproducible; releases are manual but straightforward.
Where we would start
Release models through a CI/CD pipeline with tests and rollback.
Deliverables
What you receive
- Release pipelines for models and prompts, with rollback
- A model registry with lineage back to the data
- Monitoring dashboards and alerts with named owners
- A retraining process with an evaluation gate
- Runbooks and an incident process
- Usage and cost reporting by system
- Regular performance reports for each business owner
- Documentation your team can operate from
Alongside operations
Where the work goes next
- AI Product EngineeringMachine Learning DevelopmentBuild the next model on the same pipelines, registry and monitoring.
- Enterprise AI IntegrationAI Governance & Risk ManagementFeed monitoring results and incidents into your AI risk register and reviews.
- Enterprise AI IntegrationData Foundations for AIAdd data-quality checks upstream, so problems are caught before they reach a model.
Questions
Questions about
MLOps & AI Operations
Can you operate models and AI systems you didn't build?
Yes. We start with an onboarding review of the system, its data, its tests and its documentation, and close the gaps that would make it unsafe to operate before we take it on.
Which platforms do you use?
The ones you already run where they fit, such as MLflow and your cloud provider's machine learning service. Any change we recommend comes with the reason for it.
How is operating generative AI different?
There is often no model to retrain. Quality depends on prompts, retrieved content and the provider's model version, so each of these is versioned, and the evaluation set is rerun whenever any of them changes.
What support hours do you provide?
Support hours, response times and escalation are agreed for each system in the support agreement, according to how critical it is.
How is a project priced?
Well-defined scopes are delivered as fixed-price engagements; when requirements are still evolving, we provide a dedicated team instead. Either way, the free technical consultation ends with a plan covering tech stack, architecture, timeline and budget, so you know the cost before work starts.
AI Product Engineering
Other services in this line
- AI Proof of Concept & MVPA small working version with agreed success criteria, to test value and feasibility before a full build.
- Generative AI DevelopmentAssistants, search and agents built on large language models, grounded in your own content and tested before release.
- Machine Learning DevelopmentPrediction, classification and recommendation models trained on your data, validated on data held back and explained.
- Intelligent Process AutomationProcesses redesigned and automated with rules, AI and human approval at the steps that need judgement.
Ready to talk about MLOps & AI Operations?
Start with a free technical consultation: a plan covering the right approach, architecture, timeline and budget for your AI work.
Start a project