Compliance-first delivery
From notebook to production, responsibly
Most machine-learning projects stall between a promising notebook and a dependable service. The data is scattered and inconsistent, nobody can reproduce last month’s training run, the model works on the laptop but times out in production, and there is no way to tell when its accuracy quietly degrades. The bottleneck is almost always engineering, not algorithms.
We build the plumbing that makes AI dependable. Batch and streaming pipelines bring data into a warehouse or lakehouse with tests and lineage (the same foundations our data and analytics team uses for BI). Training runs are versioned and reproducible; models are packaged and served behind autoscaling endpoints on AWS, Azure or GCP; and evaluation, drift and cost monitoring are wired in from the start. For retrieval-augmented generation we build embedding pipelines and vector search, applying the practices described in our guide to RAG in production with guardrails.
You end up with an ML platform your data scientists can ship through on their own: reproducible pipelines, a model registry, monitored inference and documented ownership. When the goal is an LLM-powered assistant or agent rather than a classic model, our generative AI team builds on the same foundations.
- Tested batch and streaming data pipelines
- Reproducible training with experiment tracking and a model registry
- Autoscaling model serving on AWS, Azure or GCP
- Embeddings and vector search for RAG and semantic search
- Monitoring for drift, data quality, latency and cost
What's Included
Everything you need, delivered by a senior, security-first team.
Data Pipelines
Ingestion and transformation with orchestration, data tests, lineage and alerting.
Lakehouse & Features
Warehouse/lakehouse design and feature tables shared by training and inference.
Model Serving
Containerised inference endpoints with autoscaling, batching and canary releases.
Vector Search
Chunking, embedding pipelines and vector databases for retrieval and semantic search.
MLOps
CI/CD for models, experiment tracking, model registry, evaluation gates and rollbacks.
Model Monitoring
Drift, quality, latency and cost dashboards with alerts and retraining triggers.
Production AI starts with trustworthy data
Most AI initiatives stall not because the model is wrong but because the data is late, inconsistent or impossible to trace, and because nobody planned how a notebook becomes a monitored production service. AI and data engineering is the discipline that fixes both: pipelines that deliver clean, documented data on schedule, and the MLOps practices that deploy, monitor and retrain models safely.
When you need AI and data engineering
- Data scientists spend most of their time finding and cleaning data.
- Models perform well in experiments but have never reached production, or nobody knows how they are performing now.
- Reports from different teams disagree on basic numbers.
- You want to build retrieval-augmented generation (RAG) or AI features on your own documents and data.
- Sensitive or regulated data needs lineage, access control and audit trails.
What we build
Data pipelines
Batch and streaming ingestion from databases, SaaS applications, files and event streams using tools such as Airbyte, Fivetran, Kafka, AWS Glue or custom Python, with orchestration in Airflow, Dagster or Prefect. Pipelines are tested, idempotent and alert on failure.
Warehouses and lakehouses
Modelled data in Snowflake, BigQuery, Redshift, Databricks or PostgreSQL, transformed with dbt, with data-quality tests and documentation. For dashboards and reporting on top, see data and analytics services.
Machine learning in production (MLOps)
Experiment tracking, a model registry, reproducible training pipelines, model serving behind APIs, and monitoring for latency, data drift and prediction quality, using MLflow, SageMaker, Vertex AI or Azure Machine Learning.
Vector search and AI-ready data
Document ingestion, chunking, embedding and indexing in pgvector, OpenSearch or a managed vector database, with metadata and permissions preserved so AI features only retrieve what a user is allowed to see. Our generative AI team builds the assistants and agents on top.
Data architecture options
| Pattern | Strengths | Best for |
|---|---|---|
| Cloud data warehouse | SQL-first, fast analytics, simple governance | BI and structured reporting |
| Data lake | Cheap storage for any format | Raw, semi-structured and ML training data |
| Lakehouse | Warehouse-style tables on lake storage | Mixed analytics and ML workloads at scale |
| Operational database + pgvector | Minimal moving parts | Smaller AI features and early-stage products |
Governance and security
We apply least-privilege access, encryption, column-level masking for personal data and lineage tracking, and we document data flows for HIPAA, GDPR or SOC 2 programmes — see HIPAA, GDPR and SOC 2 compliance.
Common pitfalls
- Building models before a reliable data pipeline exists.
- No monitoring after deployment, so model quality silently degrades.
- Copying sensitive data into notebooks and vector stores without access controls.
- Over-engineering a “data platform” before there is a clear use case.
Our article Taking RAG From Prototype to Production covers the evaluation and guardrail work needed for AI features specifically.
Deliverables
- Data architecture and pipeline code in your repositories.
- Modelled, tested and documented datasets.
- ML training, deployment and monitoring pipelines.
- Runbooks and handover to your data team.
From Prototype to Production ML
Assess
Review data sources, existing models and infrastructure; define success metrics with the business.
Data Foundations
Build reliable pipelines and datasets with tests and lineage.
Productionise
Package, serve and integrate the model with CI/CD and evaluation gates.
Monitor & Iterate
Track drift and performance, retrain on schedule or trigger, hand over runbooks.
Frequently Asked Questions
Data engineering builds and runs the pipelines, storage and infrastructure that deliver reliable data. Data science and machine learning use that data to build models and insights. Without solid data engineering, models rarely make it to production.
Common choices include Airflow, Dagster, dbt, Kafka, Airbyte and Fivetran for pipelines; Snowflake, BigQuery, Redshift, Databricks and PostgreSQL for storage; and MLflow, SageMaker, Vertex AI and Azure ML for MLOps. We fit the stack to your scale and cloud.
Yes. We package the training code into a reproducible pipeline, register the model, serve it behind a secure API and add monitoring for latency, drift and prediction quality, with a process for retraining.
With least-privilege access, encryption, masking or pseudonymisation of personal data, lineage tracking and audit logs, and by keeping data in your own cloud accounts.
Usually as a fixed-price assessment followed by phased delivery, either fixed-price per phase or a dedicated team billed monthly. Cloud and tool licence costs are billed to your own accounts.
Book a free consultation. We identify a valuable first use case, review the data sources involved and propose a plan to deliver it end to end.
Ready to get started with AI & Data Engineering?
Book a free consultation — we'll map a secure, practical path forward.