DevOps & MLOps Managed Services
Internal platform teams are hard to staff and easy to lose. When the pipeline breaks and the person who built it has moved on, delivery stops across every team at once.
We operate the delivery platform as a service: build and deployment pipelines, environments, release process and the model deployment path that AI work depends on.
Platforms we typically deliver this on
Scope of the service
CI/CD operations
Build and deployment pipeline management across GitHub Actions, Azure DevOps, GitLab and Jenkins.
Environment management
Provisioning, refresh, data masking and lifecycle for development, test and staging environments.
Release management
Controlled promotion with approval gates, change records and rollback.
MLOps
Model registry, automated training and deployment pipelines, evaluation gates and staged rollout.
Observability
Logging, metrics and tracing standards implemented consistently across services.
Developer experience
Reducing build times, flaky tests and manual steps — measured, because delivery speed is a real cost.
Where this is used
Representative engagements, described at the level our clients permit. Sector and shape are accurate; identifying detail is withheld.
Automating a manual release process
Releases were a scheduled evening event requiring several people. We automated the pipeline with approval gates and automated rollback.
Outcome — Releases moved from an event to a routine, with rollback tested.
Environments that match production
Test environments had drifted from production, so defects appeared only after release. Infrastructure as code and automated refresh with masked data closed the gap.
Outcome — Defects found in test rather than in production.
A model deployment path with evaluation gates
Models were deployed manually with inconsistent validation. We built a registry and pipeline where evaluation thresholds must pass before promotion.
Outcome — No model reaching production without passing its evaluation suite.
Before you get in touch
Do you replace our platform team?
Usually we supplement one. A common pattern is that we run the platform while internal engineers focus on product delivery.
Which toolchains do you support?
GitHub Actions, Azure DevOps, GitLab CI, Jenkins, Terraform, Kubernetes, MLflow and the native services of the major clouds.
Can you migrate us to a new toolchain?
Yes, usually incrementally — running both in parallel and moving pipelines in batches rather than stopping delivery for a cutover.
Often engaged alongside this
AI Managed Services
Production AI degrades quietly. We monitor quality, catch drift, control spend and retrain — under service levels that cover output quality, not just availability.
Read moreManaged ServicesCloud & Infrastructure Managed Services
Managed infrastructure across Azure, AWS and Google Cloud — monitoring, patching, security posture, resilience and continuous cost management.
Read moreAIAI Architecture & Platform Engineering
Model choice is the easy part. We design and build the platform layer — data access, identity, evaluation, observability, cost control — that everything else depends on.
Read moreStart with three weeks and a straight answer
The AI Readiness Assessment is fixed in scope, fixed in price and produces four deliverables you own — whether or not you continue with us.