AI Code Review & Risk Scoring
Every pull request gets an AI review for bugs, security issues and risky changes, plus a risk score that decides how carefully it is rolled out.
✦ AIOps · MLOps · GenAI
DevOps, supercharged with AI.
We use AI tools at every step of delivery — from reviewing code to forecasting traffic — so your releases ship faster, incidents get fixed sooner and your ML models reach production reliably.
AI-powered delivery
Where AI speeds up your deployments
Automation handles the repetitive work; AI handles the decisions that used to need a human watching dashboards.
Smart Test Selection
AI picks the tests that matter for each change and flags flaky ones, so pipelines finish in minutes without losing coverage.
Automated Canary Analysis
AI compares error rates, latency and business metrics of the new version against the old one and promotes or rolls back automatically.
Predictive Auto-Scaling
Models learn your traffic patterns and add capacity before the peak, then scale down afterwards to keep cloud bills low.
Anomaly Detection & Root Cause
AIOps groups thousands of alerts into a few real incidents and points to the likely cause: the deploy, the config change or the noisy neighbour.
AI Copilots & ChatOps
AI assistants generate Terraform, Dockerfiles, pipelines and runbooks, and answer “what changed?” right inside Slack or Teams.
MLOps & LLMOps
From notebook to production — reliably, every time.
Most ML models never make it to production. We build the pipelines, platforms and guardrails that take your models live and keep them accurate.
- Automated training pipelines and experiment tracking
- Model registry, versioning and approval workflows
- One-click deployment to real-time or batch endpoints
- Drift, accuracy and cost monitoring with auto-retraining
- GPU auto-scaling for training and inference
- LLM apps, RAG and chatbots with safety and cost guardrails
- 1
Data
Versioned datasets and feature pipelines
- 2
Train
Reproducible training on scalable compute
- 3
Validate
Automated accuracy, bias and performance checks
- 4
Deploy
Canary or shadow rollout with zero downtime
- 5
Monitor
Drift detection triggers retraining automatically
How AIOps works
From alert storm to fixed — in minutes
- 1
Collect
Metrics, logs, traces and deploy events flow into one place.
- 2
Detect
ML models spot unusual behaviour before thresholds are crossed.
- 3
Diagnose
Related alerts are grouped and the probable root cause is identified.
- 4
Resolve
Runbooks fix known issues automatically; engineers get full context for the rest.
AI tools we work with
The right AI for each job
MLOps platforms
MLflowKubeflowSageMakerVertex AIAzure ML
Model serving
KServeBentoMLRay ServeTritonvLLM
GenAI & LLMs
Amazon BedrockAzure OpenAIOpenAILangChainLlama
AIOps
Datadog WatchdogDynatrace DavisNew Relic AIPagerDuty AIOps
AI coding assistants
GitHub CopilotAmazon Q DeveloperCursor
Data & features
DVCFeastAirflowSpark