Without MLOps, models degrade silently as data shifts, experiments are unreproducible, and deployments are manual and risky. We build the automation and observability that lets your data science team ship reliably.
Discuss Your Project →Every layer of the MLOps stack — from experiment tracking to production monitoring — with full documentation handover so your team owns the platform.
Automated pipelines that retrain on new data, run evaluation gates before promotion, and deploy with canary testing.
Full experiment reproducibility with tracked hyperparameters, datasets, metrics, and model artefacts — every experiment auditable.
Production model serving with auto-scaling, health checks, A/B routing, and SLA monitoring at any scale.
Real-time feature serving with point-in-time correctness — eliminating train/serve skew that silently degrades accuracy.
Models degrade silently. We build drift detection, performance monitoring, and automated retraining so you catch regression before users do.
Set up and optimise cloud ML infrastructure — from managed platforms to cost-optimised GPU clusters on Kubernetes.
Best-in-class tools chosen for performance, reliability, and team expertise — not hype.
A clear, collaborative process with no surprises and working demos at every milestone.
Review current model deployment state, identify gaps in reproducibility and monitoring, and design the target MLOps architecture.
Deploy MLflow or W&B for all existing models. Dataset versioning with DVC. Immediate reproducibility improvement.
Automated training pipelines triggered by data updates or schedules with evaluation gates and model registry integration.
Production inference infrastructure with auto-scaling, health checks, A/B model routing, and latency SLA monitoring.
Data drift detection, model metric dashboards, PagerDuty and Slack alerting, and automated retraining trigger setup.
Full documentation, runbooks, team training, and handover so your data science team owns the platform independently.
No juniors, no mid-weight delegation. Every engineer on your project is 5+ years experience, senior by any measure.
We set Lighthouse 90+ as a non-negotiable acceptance criterion — not a target, a requirement. Deployments fail if CWV regress.
Unit, integration, and E2E tests as standard deliverable. We don't ship without coverage. No exceptions under deadline pressure.
Full system design — schema, API contracts, auth, deployment — documented and approved before any code is written.
WCAG 2.1 AA from component 1, not added at the end. Keyboard navigation, screen readers, colour contrast — non-negotiable.
End of every sprint, you get a live staging URL to click through. Not a Loom recording — a real deployed demo.
100% IP & code transfer. Your repo, your infra, your AWS account. Full documentation so your team can own it the day we hand over.
GA4, Mixpanel or Amplitude wired in before go-live. You launch with data, not waiting weeks to set up tracking after.
"They architected and built our entire web platform from scratch — real-time collaboration, complex permissions, WebSockets. Every edge case handled, zero bugs at launch."
"Our new storefront loads in 0.8s and converts at 3.2x our old Magento site. Every detail considered — mobile-first, accessibility, structured data. The results speak."
"From Figma to deployed in 8 weeks. Their React architecture thinking sets them apart from every agency I\"
"200K concurrent users on launch day — not a single outage. The infrastructure and caching strategy Nexcode built handled load I didn\"
"The real-time dashboard processes 1M+ events/day without a hiccup. Clean code, exceptional docs, and they explained every architectural decision. Extended the team afterward."
Have a question not covered here? Book a free 30-min call →
Free MLOps audit. We review your current model deployment state, identify the highest-impact improvements, and provide a fixed-price proposal for the first phase.