MLOps and LLMOps that keep AI systems reliable after launch day.
Models drift, providers update, data changes and costs creep. We set up the evaluation gates, release process, monitoring and traceability that let you change AI systems with confidence — for both predictive machine learning and generative applications.
- Evaluation pipeline
- Release process
- Production monitoring
- Tracing and lineage
- Retraining and refresh workflows
- Runbooks and incident response
Where this helps
What we deliver
How it works
- 01
Audit current operations
We review how models and prompts are built, tested, released and monitored today, and where the gaps carry the most risk.
- 02
Define quality measures
With product owners, we agree what good output means and how to measure it automatically and through sampled human review.
- 03
Build the pipeline
Evaluation, release and monitoring tooling is set up in your environment and wired into your CI/CD.
- 04
Migrate existing systems
Current models and AI applications are brought under the new process one at a time.
- 05
Operate or hand over
We run operations under an agreed scope or train your team to do so, with regular reviews of quality and cost.
Design decisions we make with you
What to measure
Offline test scores, online user signals and business outcomes each tell part of the story. We connect them. See [measuring AI value](/insights/measuring-ai-value).
Human review sampling
How many production outputs people review, how they are selected, and how their ratings feed back into test sets.
Build or buy tooling
Open-source, managed or existing platform tools — chosen to fit your stack rather than adding another vendor by default.
Provider model changes
Pinning model versions where possible and re-running evaluations before adopting a provider's update.
Cost attribution
Tagging usage by feature and team so spend can be tied to value and optimized where it matters.
Applications
Related capabilities
- Enterprise Knowledge AssistantAnswer staff questions from your own policies, procedures and records, with sources shown and access rules respected.
- Demand ForecastingForecast demand by product, location and week, with the uncertainty visible, so planners can make and explain their decisions.
- Fraud & Risk SignalsScore transactions, claims or applications for risk and send the suspicious ones to investigators with the reasons attached.
- Machine Learning & Predictive AnalyticsForecasting, classification, anomaly detection and recommendation models trained on your data and evaluated against the decisions they support.
- Generative AI & LLM DevelopmentApplications built on large language models that are grounded in your data, tested against real cases and costed before launch.
- Enterprise & Private AIAI deployed with the data isolation, access control and operational ownership your security and compliance teams require.
- Responsible AI & GovernancePractical ownership, review, privacy and audit controls that let teams ship AI without losing track of what it does.
- Measuring the value of an AI systemMeasure an AI system against a baseline taken before launch, using a small set of outcome metrics, quality guardrails that must not get worse, the full cost per unit of work, and adoption. Be explicit about what the numbers can and cannot attribute to the AI.
- From AI pilot to production: a readiness checklistA pilot is ready for production when it has a named owner, an evaluation set that reflects real work, integrations that run under their own identity, documented controls, and a plan for monitoring, cost and support. If any of those are missing, fix them before you scale.
Questions buyers ask
MLOps covers models you train — data pipelines, retraining, drift detection. LLMOps covers applications built on language models — prompt versioning, evaluation of open-ended output, provider changes and token cost. Many organizations need both.
Yes. We start with an audit, then bring existing models and applications under evaluation and monitoring without rebuilding them.
A mix of rule-based checks, reference comparisons, model-graded scoring calibrated against human ratings, and sampled human review for what automation cannot judge.
No. We start with the pieces that address your biggest risks and use tools that fit your existing stack. Infrastructure work is shared with our cloud & DevOps team.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.