What we deliver
We ensure your AI models are production-ready, reliable, and cost-effective. From low-latency serving to comprehensive governance, we handle the engineering so you can focus on the models.
Serving, monitoring and governance so your AI runs fast, safe and cost efficient.
Serving
+GPU or CPU inference, autoscaling, canary releases and rollbacks for reliable uptime.
Observability
+Full tracing, metrics and logs for models, prompts and tools to understand system behavior.
Monitoring
+Tracking quality, drift, latency and cost with configured alerting and incident runbooks.
Governance and security
+Implementation of access control, audit trails and policy enforcement for compliance.
Data and model lifecycle
+Managed registries, versioning and reproducible pipelines for consistency.
Cost and latency optimisation
+Implementation of caching, batching and quantisation where sensible to reduce overhead.
Architecture at a glance
Platform
+Infrastructure based on Kubernetes, Terraform and GitOps practices.
Registry
+Centralized datasets and models with lineage tracking and approval workflows.
Pipelines
+CI and CD for data and models with staged environments (dev, stage, prod).
Serving
+Exposed via REST or gRPC endpoints, supported by feature stores and caching layers.
Controls
+Secret management, RBAC, network isolation, rate limits and usage quotas.
SLOs and runbooks
We define standards to ensure operational excellence and rapid incident response.
Define SLOs
+Establish clear Service Level Objectives for latency, availability and quality.
Create runbooks
+Documented procedures for deployment, rollback and incident handling.
Add playbooks
+Specific guides for data and model drift with assigned owners and timer-based responses.
Process
Our Case Studies
MLOps & AI Production Engineering - Frequently Asked Questions
- What is MLOps and why does it matter for production AI?
- MLOps is the discipline of keeping AI models reliable, observable, and cost-efficient after deployment. Without it, models degrade silently as data distributions shift, costs grow unpredictably, and incidents are hard to diagnose. MLOps covers the full lifecycle: versioned datasets, experiment tracking, CI/CD for models, serving infrastructure, drift monitoring, and rollback procedures.
- What does a production MLOps stack include?
- A production MLOps stack typically covers: Kubernetes-based serving infrastructure with autoscaling and canary releases, a model and dataset registry with lineage tracking, CI/CD pipelines for data and model updates, observability tooling (traces, metrics, logs), drift detection with alerting, and access controls (RBAC, audit trails, rate limits). We select tools based on your cloud/on-prem constraints and team capabilities.
- How do you handle model drift in production?
- We define data quality checks and performance SLOs before deployment. Monitoring pipelines track statistical drift (feature distribution shifts), prediction drift (output distribution changes), and business metric degradation. When thresholds are crossed, alerts trigger playbooks with defined owners and response timelines - either automatic retraining or human review depending on severity.
- Can you set up MLOps for on-premises or private cloud deployments?
- Yes. We work with on-premises GPU clusters, private cloud environments (VMware, OpenStack), and air-gapped setups where data cannot leave your infrastructure. The tooling stack is selected based on your constraints - we don't require any specific cloud provider's managed services.
