MLOps
The problem
Getting model to production is easy. Keeping it accurate and cost-efficient over time is where most teams struggle.
What we do
Full ML lifecycle management. Pipeline automation, continuous training, drift detection, scalable serving.
What you get
Models stay accurate without manual work. GPU costs predictable. Drift caught before users notice.
What's included
LLM Observability and Performance
The problem
Traditional monitoring says service is up. Nothing about LLM quality. Response degrading? Prompts underperforming? No visibility.
What we do
LLM-specific observability. Quality monitoring, latency tracking, prompt performance, output anomaly detection.
What you get
Visibility into real user LLM experience. Degradation caught before churn. Latency traced to source.
What's included
GPU and AI Infrastructure Cost Management
The problem
GPU costs become the most surprising line on the AWS bill. No visibility, no optimisation, no plan until CFO asks.
What we do
Audit AI infra spend. Spot instance strategy, inference optimisation, right-sizing, FinOps for GPU workloads.
What you get
Cost structure that never becomes the reason you slow down hiring or product investment.
What's included
Proven results
80%
fraud detection rate
3×
faster model updates
50%
lower infra costs
FinTech Startup, Series A. Engagement under NDA. Client name withheld by request.
Does your AI model work in development but underperform in production?
That gap is almost always an MLOps problem, not a model problem.