AI Infrastructure & MLOps
Building the model is half the job. Keeping it reliable is the other half.
Deployment, monitoring, and retraining for models and agents already in production — the same reliability discipline behind our cloud and DevOps work, applied specifically to AI.
Who This Service Is For
This service is for businesses running AI in production already — whether we built it or someone else did — that need it to stay reliable as usage grows.
Businesses With AI Already in Production
Businesses Scaling AI Usage
Problems We Solve
The reliability gaps that separate a working pilot from a production system.
Models Degrading Silently Over Time
Accuracy that quietly drifts downward as real-world data shifts away from what the model was originally trained on — with no alert until something visibly breaks.
No Visibility Into Production AI Performance
Models and agents running live with no dashboard, no alerting, and no clear answer to "is this actually working right now."
Manual, Fragile AI Deployment
Updating a model or agent that requires manual steps and carries real risk of breaking something in production.
Unpredictable and Growing API or Compute Costs
AI spend that scales with usage in ways that aren't tracked, budgeted, or optimised.
Pilot-Stage AI Never Hardened for Production
Proof-of-concept AI that worked in a demo but wasn't built with the monitoring, security, or reliability a production system needs.
Multiple Models or Agents, No Unified Oversight
Several AI systems running independently, each with its own ad hoc setup, and no single view across all of them.
What You Get
Every infrastructure engagement delivers these three groups of outputs.
Infrastructure Assessment
- Audit of current AI deployment, monitoring, and reliability gaps
- Cost analysis across models, agents, and API usage
- Scaling and reliability requirements defined
Deployment & Monitoring Setup
- Production-grade deployment pipelines for models and agents
- Performance and drift monitoring, with alerting
- Cost tracking and optimisation across AI workloads
Ongoing Operations
- Scheduled retraining pipelines as data evolves
- Incident response for AI system failures or degraded performance
- Continuous optimisation as usage scales
How Your AI Infrastructure Gets Set Up
Five phases from assessment to ongoing operations — structured so AI in production gets the same reliability discipline as the rest of your stack.
How Your AI Infrastructure Gets Set Up
Tech Stack
Deployment, monitoring, and orchestration tooling built for AI running at production scale.
Related Case Studies
Real platforms built for real businesses — see how we've delivered results.
Frequently Asked Questions
Find answers to common questions about our services, process, and how we can help your business.
Yes — this service is scoped around your current systems regardless of who built the original model or agent.
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