MLOps infrastructure that keeps models honest in production.
Experiment tracking, model registry, CI/CD for ML, drift monitoring, and deployment automation — so your data science team ships once and your operations team trusts what runs in prod.
Models fail quietly. We build the MLOps layer that makes failure loud: versioned datasets, reproducible training pipelines, gated promotions to production, real-time drift detection, and rollback paths your on-call engineer can execute at 2am.
Whether you are maturing a single critical model or standing up an ML platform for a portfolio of use cases, we design the registry, serving, and observability stack to match your cloud, compliance, and team maturity.
Production-grade engineering outcomes
Scoped deliverables with clear acceptance criteria — not open-ended consulting hours.
Experiment Tracking
MLflow, W&B, or custom tracking with reproducible run configs and artefact storage.
Model Registry & Governance
Versioned models, approval workflows, and audit trails for regulated environments.
CI/CD for ML
Automated train, eval, and deploy pipelines with quality gates and canary releases.
Model Monitoring
Drift, performance decay, data quality, and latency dashboards with alerting.
Serving Infrastructure
Triton, BentoML, Seldon, and Kubernetes-native inference with autoscaling.
Cost & GPU Optimisation
Batching, quantisation, spot-instance strategy, and inference right-sizing.
The tools we deploy in production
Battle-tested frameworks, platforms, and infrastructure — chosen for reliability, not hype.
MLOps Platforms
Serving
Monitoring
Data Quality
CI/CD & Infra
Optimisation
How enterprises buy and scale this capability
Clear engagement models, buyer alignment, and commercial outcomes — so procurement and engineering stay in sync.
Buyer Personas
- Head of ML Platform standardising how models reach production
- CTOs whose ML POCs keep stalling at the deployment cliff
- Compliance-heavy teams needing model lineage and approval gates
- Cost-conscious infra leads optimising GPU spend across ML workloads
Engagement Models
- MLOps maturity assessment with 30-day remediation roadmap
- Platform build for first 1–3 production models with templates for scale
- Migration from notebook-driven deploys to registry-based CI/CD
- Managed MLOps retainer with monitoring, retraining, and incident response
Commercial Outcomes
- Faster model promotion cycles with automated eval gates
- Reduced production incidents via drift alerts before user impact
- Lower inference cost through right-sized serving and quantisation
- Audit-ready lineage for procurement, compliance, and board review
Procurement Fit
- Works with your existing cloud — AWS, GCP, Azure, or on-prem K8s
- No proprietary platform lock-in; open-source first architecture
- Documentation and runbooks for internal platform team handover
- India-based senior MLOps engineers with enterprise delivery record
Where this lands in the real world
Enterprise ML Platform
Central registry and deployment fabric for multiple business-unit models.
Regulated Industries
Approval workflows and audit trails for finance, health, and defence ML.
High-Volume Inference
Autoscaling serving with latency SLOs and cost dashboards.
Edge + Cloud Hybrid
Coordinated deploy pipelines for edge and cloud model variants.
LLM Operations
Prompt versioning, eval suites, and cost tracking for generative workloads.
Model Rescue
Stabilise failing production models with monitoring and rollback automation.