What We Deliver

Production Deployment for ML & LLM Systems

Containerized deployment, scalable infrastructure, and clear release processes for model updates — built for real-world traffic and reliability.

CI/CD for Models

Automated pipelines for model training, evaluation, versioning, and safe rollouts (blue/green, canary), aligned with modern engineering practices.

Monitoring & Observability

We set up monitoring for latency, errors, cost, drift, quality metrics, and usage patterns — so you can detect issues before they impact users.

Governance & Security

Access control, auditability, environment separation, secrets handling, and safe data flows — designed for regulated environments.

Cost & Performance Optimization

We optimize inference cost, scaling strategy, caching, batching, and model selection to balance quality with budget.

Common Problems We Fix

• “We have a model, but it’s not production-ready.” We turn PoCs into reliable systems.
• “Deployments are risky and break things.” We introduce safe release patterns and automated validation.
• “Quality degrades over time.” We implement drift monitoring and retraining workflows.
• “Costs are unpredictable.” We set budgets, monitoring, and optimization strategies.

How We Work

MLOps Assessment (1–2 Weeks)

We review your current pipelines, infrastructure, and model lifecycle. Deliverable: a prioritized roadmap to reach production-grade MLOps.

Implementation Sprint (3–6 Weeks)

We implement the core pipelines: deployment, monitoring, release strategy, and operational playbooks.

Ongoing MLOps Retainer

Continuous improvements, cost optimization, monitoring tuning, incident response support, and delivery of new production features.