Data & AI operations: MLOps and LLMOps
Keep your data trustworthy and your AI reliable after launch – data platforms, model and prompt operations, and monitoring of every AI answer, its quality and its cost. You know when data, quality or cost drifts – and can fix it before users notice.
Most AI fails after launch, not before it
Models drift, prompts change, data goes stale and costs creep up. AI in production needs the discipline of software – versioning, testing, monitoring and clear owners – extended to data, models, prompts and agents.
of AI projects unsupported by AI-ready data will be abandoned through 2026, Gartner predicts.
Source: Gartner, February 2025
- BeforeModels deployed and forgottenNowModels, prompts and data versioned and tested together
- BeforeQuality judged by complaintsNowEvery AI answer traced, scored and monitored
- BeforeAI costs found on the invoiceNowThe cost of each AI feature tracked and controlled
MLOps or LLMOps: what does each need to run well?
Generative AI adds new things to version, test and watch.
| Area | Classic machine learning (MLOps) | Generative AI (LLMOps) |
|---|---|---|
| What you version | Data, features and model weights | Prompts, retrieval indexes, model versions and tools |
| How quality is measured | Accuracy on labelled test data | Evaluation sets, human review and automated checks |
| Main risks after launch | Data drift and model decay | Wrong answers, prompt injection and cost spikes |
| Main cost driver | Training and serving infrastructure | Tokens per request and the choice of model |
| What you monitor | Prediction quality and drift | Every answer – quality, safety, latency and cost |
What this service covers
Each capability can be used on its own or combined with capabilities from other services. “Also part of” shows the other services that include it.
AI-ready data platforms
Pipelines, data quality, lineage and shared definitions – the foundation that makes AI answers and dashboards trustworthy.
MLOps & LLMOps
Version models, prompts and data together; automate testing, release and retraining; route each request to the right model.
AI observability & cost control
Trace every AI answer and agent action, track quality and drift, and see what each AI feature costs.
Analytics & decision dashboards
Dashboards and plain-language questions over your data, so teams act on numbers instead of waiting for reports.
Combined with capabilities from other services
This service draws on capabilities owned by other teams. Each is explained in full on its own service page.
How we deliver it
- 01
Assess
Data sources, quality, pipelines and how models reach production today.
- 02
Build the foundation
Pipelines, data quality checks and one platform for models and prompts.
- 03
Automate
Testing, release and retraining, with approvals where the risk is high.
- 04
Monitor and improve
Quality, drift and cost dashboards, with alerts and regular reviews.
- Reliable data pipelines with quality checks and lineage
- Automated release of models and prompts behind evaluation gates
- Monitoring of AI quality, drift and cost
- Dashboards your teams use to make decisions
- Start with one capabilityA fixed-scope piece of work, such as an assessment, a prototype or a proof of concept.
- Combine capabilities into one projectCapabilities from several services, delivered by one team with one plan and one point of contact.
- Add a dedicated teamEngineers who join your team to build and run it with you.
Selected client work
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Read the articleFrequently asked questions
What is MLOps?
MLOps is the set of practices and tools that take machine learning models from experiments to reliable production: versioned data and models, automated training and deployment, monitoring for drift and accuracy, and governed retraining.
What is LLMOps, and how is it different?
LLMOps covers the extra work for generative AI: managing prompts, retrieval data and model versions, running evaluations for quality and safety, tracing agent actions and controlling token costs.
Why do AI models fail after launch?
Data changes, upstream systems change, model providers update their models and nobody owns monitoring. We set up pipelines, alerts and evaluations so quality is measured continuously and issues are fixed before users notice.
Do you build BI dashboards as well as ML?
Yes. We unify data from CRMs, analytics, ad platforms and spreadsheets into Looker Studio or Power BI dashboards, as we did for The Junkluggers and Beplace – on the same data foundation as your AI, so the numbers match.