Data Engineering & MLOps
Pipelines, feature stores, model deployment and monitoring so AI keeps working after launch.
AI systems are only as reliable as the data feeding them and the operations around them. We build the pipelines that deliver clean, timely data, the infrastructure that deploys models repeatably and the monitoring that shows when quality drifts. It is unglamorous work and it is the reason production systems survive.
The same foundation serves analytics, so the investment pays off beyond the AI programme.
- We treat prompts and evaluation sets as code, versioned and tested like any other release.
- Data quality checks run in the pipeline, so bad data is stopped before it reaches a model.
- The platform is built with our IT and DevOps practice, so it fits your wider infrastructure.
- A group whose AI programme has stalled because every model needs its own data extract.
- A bank or retailer with models in production that nobody monitors for drift.
- A company standardising analytics and AI on one platform after years of departmental tools.
- Each proof of concept starts with weeks of manual data preparation.
- A model degraded for months before anyone noticed.
- Prompts and evaluation sets live in documents rather than in version control.
- Data quality problems are found by the model's users.
What is included.
- 01
Data platform
Warehouse or lakehouse with ingestion, modelling and quality checks.
- 02
Pipelines
Batch and streaming pipelines with orchestration, testing and lineage.
- 03
Feature and vector stores
Reusable features and embeddings managed with versioning.
- 04
Model deployment
CI/CD for models and prompts, with staged rollout and rollback.
- 05
Monitoring
Data quality, model performance, drift and cost tracked with alerts.
Four steps, no surprises.
- 01
Assess
Current data, tooling and operational gaps reviewed.
- 02
Design
Platform architecture, pipeline patterns and operating model.
- 03
Build
Pipelines, stores and deployment automation delivered in increments.
- 04
Operate
Monitoring live, team trained and hand-over or ongoing support.
From first meeting to steady state.
- 01Weeks 1 to 2
Assess
Current data, tooling and operational gaps reviewed with your data and platform teams.
- 02Weeks 3 to 4
Design
Platform architecture, pipeline patterns and operating model agreed.
- 03Weeks 5 to 14
Build
Pipelines, stores and deployment automation delivered in increments alongside live use cases.
- 04Week 15 onwards
Operate
Monitoring live, team trained and either hand-over or an embedded team.
- Time from a new data source to a modelled, tested table.
- Pipeline failure rate and time to detect.
- Time from a model or prompt change to a monitored deployment.
- Drift and quality incidents caught by monitoring before users report them.
- Data platform architect
- Data engineers
- MLOps engineer
- Analytics engineer
- Data platform and modelled tables.
- Orchestrated pipelines with tests.
- Model and prompt CI/CD.
- Observability dashboards and alerts.
- Documentation and runbooks.
Platform work is fixed scope after a two-week assessment and typically runs eight to sixteen weeks for a foundation. Ongoing pipeline development and operations run on a retainer or as an embedded team for larger programmes.
Systems Integration & Data Infrastructure
Connect ERP, CRM, e-commerce and data sources through APIs and pipelines so data moves without hands.
IT ConsultingDevOps & Platform Engineering
CI/CD, infrastructure as code and observability so teams ship safely and often.
AI Consulting & AutomationCustom LLM & RAG Solutions
Assistants, copilots and knowledge tools grounded in your own documents and data.
Data Engineering & MLOps, in plain terms.
dbt, Airflow or Dagster, Databricks or BigQuery, MLflow, Langfuse and the cloud services around them. We choose for fit with your team and existing stack.
Some of it. The discovery workshop identifies the minimum data work needed for the first use case, and the platform grows from there.
Yes. Many engagements are a mix of our engineers and yours, with the goal of your team owning the platform.
Tracing of every request, automated quality scoring on samples, cost tracking and alerts when performance moves outside agreed bounds.