87% of ML models never reach production. We build the infrastructure that closes that gap — feature stores, retraining pipelines, model registries, and drift monitoring.
Your data scientists build great models. But they live in Jupyter notebooks, rot in S3 buckets, and never reach the customers who need them. This is an infrastructure problem, not a talent problem.
Models trained in notebooks with no reproducibility, no experiment tracking, and no clear path to a production serving endpoint.
Data scientists hand off a pickle file. MLEs don't exist. DevOps doesn't understand ML. The model sits in staging forever.
Retraining is a calendar reminder. Someone runs a script locally, uploads a file, and prays the serving layer picks it up correctly.
The model was 92% accurate at launch. Eighteen months later it's at 61% and no one noticed because there's no drift monitoring.
From raw features to production serving to drift alerting — a complete platform your data science and ML teams actually want to work with.
Centralised, versioned feature definitions. Train-serve skew eliminated. Point-in-time correct features for offline and online serving.
Every experiment tracked. Canary deployments for models. Automated rollback on performance regression. One-click promotion to production.
Orchestrated LLM agent architectures for document processing, reasoning chains, and regulated industry workflows with full audit trails.
Statistical drift detection on every prediction. Feature distribution monitoring. Automated retraining triggers when model performance degrades.
Purpose-selected tools across the full ML lifecycle — from data versioning to production monitoring to agent orchestration.
We don't just hand you a platform. We build it with you, document every decision, and leave your team stronger than we found it.
Inventory all models, pipelines, and data flows. Identify the highest-leverage automation opportunities.
Stand up Feast or SageMaker FS. Migrate top features. Connect MLflow model registry to your CI system.
Automated retraining triggers, data validation with Great Expectations, and end-to-end pipeline testing.
Evidently AI drift dashboards, Bedrock Guardrails for LLM outputs, and runbook-driven incident response.
A global pharma client had 20 manual clinical data extraction workflows consuming 12 FTEs at 60% of their time. HIPAA requirements had blocked every previous AI proposal. They needed production-grade agents, not a prototype.
We built 200+ orchestrated Bedrock agents with full Guardrails, CloudTrail audit logging, VPC isolation, and PII redaction. 8 weeks from kickoff to HIPAA-compliant production. 94% extraction accuracy, saving $340K/year in manual labor.
Read the full case studyEvery engagement is scoped to a clear deliverable. No hourly billing. No surprise invoices.
Start with an MLOps Audit — we'll show you exactly what's blocking your models from reaching customers.
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