Data & MLOps Foundations
Models are only as good as the data and pipelines beneath them. We build the data foundations and MLOps that make AI reliable in production: clean, governed data, feature and model pipelines, and the monitoring that keeps them healthy.
AI fails at the foundations, not the model
Fragile by default
- Messy, ungoverned data
- Models that drift silently
- No repeatable path to production
Reliable in production
- Trustworthy, traceable data
- Automated, versioned pipelines
- Monitored and reproducible
Build the base that AI can stand on
We assess your data, engineer the pipelines and operationalise the lifecycle so models stay reliable in production.
Assess data readiness
Audit data quality, lineage and availability for your AI use cases.
Engineer the pipeline
Build ingestion, cleaning, feature and training pipelines on your existing stack.
Operationalise (MLOps)
CI/CD for models, versioning, deployment and reproducibility.
Monitor and maintain
Data and model monitoring, drift detection and retraining.
What we deliver
Data engineering & pipelines
Ingestion, cleaning and transformation on your stack.
Data quality & lineage
Trustworthy, traceable data with documented lineage.
Feature stores & management
Reusable, consistent features across models.
MLOps / LLMOps
CI/CD, versioning and deployment for models and LLM apps.
Model monitoring & drift
Detect performance decay and data drift in production.
Retraining & lifecycle
Automated retraining and model lifecycle management.
- Data-readiness assessments before an AI programme
- Building production data and feature pipelines
- Standing up MLOps and LLMOps and CI/CD for models
- Monitoring and observability for live models
- Moving prototypes from notebook to production
- Data lineage and quality to support governance
Fits into your existing stack
Neulaxy solutions integrate with the systems, tools, data and processes you already use, through clean and well-documented APIs. We fit into your stack rather than asking you to rip out and replace what works.
From our insights
Frequently asked questions
What is MLOps and why do we need it?+
MLOps is the practice of deploying, versioning, monitoring and retraining models reliably. Without it, models that work in a notebook fail or drift in production.
Our data is messy. Can you help?+
Yes. We assess data quality and lineage, then build the pipelines that turn messy data into a trustworthy foundation for AI.
Do you work with our existing stack?+
Yes. We integrate with your data warehouse, tools and cloud rather than asking you to rip and replace.
What is model drift and how do you handle it?+
Drift is when a model gradually loses accuracy as real-world data moves away from its training data. We monitor for it and automate retraining.
Can you take a prototype to production?+
Yes. Closing that gap, the pipelines, deployment, monitoring and reproducibility, is exactly what this work is for.
How does this relate to AI governance?+
Good data foundations and lineage are prerequisites for governance. This work produces much of the traceability that assurance and audit require.
Ready to build on solid foundations?
Book a 30-minute consultation and we will map the highest-leverage first use case.