CI/CD architecture
Fast, reliable pipelines with testing, security and eval gates — for code, models and prompts alike.
Engineering Excellence
DevOps & CI/CD for AI
Ship AI changes safely with pipelines that test quality, security, and cost before production.
- 10×deployment frequency improvements
- <15 mincommit-to-production lead time
- 75%reduction in change failure rate
Capabilities
Delivery pipelines for the AI era
Reliable release pipelines for AI applications, prompts, models and infrastructure.
GitOps and environments
Declarative deployments and on-demand environments that end release-day drama.
Quality and security gates
Tests, scans and AI evals enforced automatically — trust built into the pipeline.
DORA-driven improvement
Delivery metrics instrumented and systematically improved.
How it works
Ship daily, sleep nightly
AI systems add prompts, models and datasets to the release surface. We build pipelines that treat them all as first-class deployable artifacts.
Pipelines
Every merge proves itself
Build, test, scan and evaluate on every change — including AI-specific gates like eval scores and prompt diffs — so main stays releasable and releases stay boring.
- Parallelized pipelines with caching for fast feedback
- AI eval gates alongside unit and integration tests
- Security scanning wired in, not bolted on
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Deployment
Progressive delivery with instant undo
Canary and blue-green rollouts driven by live metrics — including AI quality signals — with automatic rollback when anything degrades. Deploys become non-events.
- Canary releases gated on latency, errors and eval scores
- One-command rollback for code, prompts and configs
- Feature flags decoupling deploy from release
Ingest→
Chunk→
Embed→
Index→
Retrieve
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Measurement
DORA metrics, honestly instrumented
Deployment frequency, lead time, change failure rate and MTTR measured from your real systems — then improved sprint by sprint with targeted fixes.
- Delivery metrics dashboards from live pipeline data
- Bottleneck analysis across build, review and deploy
- Quarterly improvement targets with visible progress
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Use cases
Where devops & ci/cd delivers value
AI delivery pipelines
Prompts, models and evals shipped with software-grade discipline.
Release acceleration
From monthly big-bang releases to daily low-drama deploys.
Monorepo at scale
Fast selective builds and tests for large shared codebases.
Environment automation
Preview and staging environments per branch, on demand.
Compliance pipelines
Segregation of duties and evidence capture without slowing delivery.
Platform team enablement
Golden-path pipelines every product team inherits for free.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
Release day used to be a war room. Now it's a merge button — sixty deploys a month, failure rate down by three quarters, and the AI evals run in the same pipeline as the unit tests.
Works with your stack
Built on the tools you already run
We integrate with your models, clouds, data platforms and enterprise systems — no rip-and-replace.
- GitHub Actions
- GitLab CI
- Jenkins
- ArgoCD
- Kubernetes
- Terraform
- Docker
- SonarQube
- Snyk
- Datadog
- LaunchDarkly
- Braintrust
FAQ
Common questions
What changes about CI/CD for AI systems?
The artifact set grows: prompts, model versions, retrieval configs and eval sets deploy alongside code. Quality gates become eval-based, and rollout decisions weigh AI quality metrics, not just errors and latency. Same discipline, wider surface.
Our pipeline takes 45 minutes. Can that be fixed?
Almost always. Caching, parallelization, test selection and right-sized runners typically cut pipeline times by 60–80%. Fast feedback changes engineering behavior more than any process mandate.
How do you reduce change failure rate?
Smaller changes, stronger gates, progressive rollout and instant rollback. When failures do happen, they touch a canary slice for minutes instead of everyone for hours.
Can you work with our compliance requirements?
Yes — approvals, segregation of duties and evidence capture are automated inside the pipeline, which auditors tend to prefer to spreadsheets and screenshots.
Next step
Make deployments boring
We'll audit your delivery pipeline and show you the path to daily, low-risk releases.