The infrastructure that keeps your models reliable after you've shipped them.
Data pipelines, feature stores, model monitoring, vector databases, and LLMOps — so your AI systems stay accurate, observable, and maintainable in production.
Most AI projects fail after deployment, not before it. Models degrade silently, training pipelines break, data quality drifts, and inference costs balloon without observability. MLOps is the discipline of keeping AI systems working reliably in production — the same way DevOps keeps web applications working. We build the data and infrastructure layers that most AI vendors skip because they only bill for the model, not for keeping it running.
85%
of ML models never make it to production without MLOps infrastructure
40–70%
reduction in inference cost with serving optimisation
10×
faster retraining cycles with automated feature stores and pipelines
What's included
Services within Data & MLOps Infrastructure
Each is a scoped engagement. Tell us which one fits your situation — or book a call and we'll scope it together.
Data Annotation & Labelling
Annotation pipeline setup, labeller onboarding, quality control workflows, and inter-annotator agreement tracking — for vision, NLP, and audio datasets.
Synthetic Data Generation
GAN-based, simulation-based, and LLM-based synthetic data production to augment scarce labelled datasets and cover rare edge cases in training distribution.
Feature Stores
Design and deployment of feature stores (Feast, Tecton, or custom) for consistent, versioned feature computation shared across training and inference — eliminating training/serving skew.
Vector Databases & Embedding Infrastructure
Vector database selection, setup, and optimisation (Pinecone, Weaviate, Qdrant, pgvector) for semantic search, RAG retrieval, and recommendation systems.
MLOps Platforms
End-to-end ML platforms using MLflow, Kubeflow, or SageMaker — covering experiment tracking, model registry, automated retraining pipelines, and CI/CD for model deployment.
Model Monitoring & Drift Detection
Production monitoring for data drift, concept drift, prediction distribution shifts, and performance degradation — with automated alerting and retraining triggers.
LLMOps & AI Observability
Tracing, latency monitoring, token cost tracking, and output quality evaluation for LLM applications in production — using LangSmith, Arize, or custom observability stacks.
The problem
Why models that worked in notebooks break in production
These aren't edge cases — they're what we hear on almost every discovery call. If any of them sound familiar, this is likely the right place to start.
Training/serving skew: features computed differently in training versus inference produce silent accuracy degradation
Data quality failures: upstream schema changes, missing values, and distribution shifts break pipelines invisibly
No monitoring: teams discover model degradation from customer complaints, not alerting systems
Retraining is manual: models go stale because updating them requires a human to run a notebook by hand
Experiment tracking is absent: teams can't reproduce results or compare model versions because nothing was logged
Who it's for
This is the right fit if…
These systems work best for organisations at a specific point — where the problem is real, the data exists, and generic tools have already proved insufficient.
Engineering teams that have built ML models but have no automated path from data to production
Data science teams whose models degrade in silence because there's no monitoring
Companies spending more than expected on LLM API costs without understanding why
Organisations with multiple models in production that no one can reliably update
Common questions
What people ask before they book
Not sure where to start?
Talk it through on a free call.
We'll help you figure out which of these fits your situation — no pressure, no obligation.
Book a Free 30-Min Call