Solutions
The gap between a model that works and a model that runs is infrastructure. BigAI builds the platform that gets models to production quickly, keeps them monitored, and keeps the bill under control.
Signals
No standard process; everything depends on whoever did it last time.
No drift monitoring, so quality decay only surfaces when the business complains.
Resources sit idle for hours a day with nobody watching.
Capabilities
A 95% accurate model sitting in a notebook creates less value than an 88% model serving thousands of decisions a day. The distance between those two states is exactly what MLOps covers.
BigAI sizes the infrastructure to your actual scale. Not every company needs a full platform on day one — but every company needs to know how its models are behaving.
Package, test and ship models through one process shared by every team.
Track data drift, concept drift, latency and prediction quality, with alerts before the business notices.
Shared features across training and serving, eliminating train–serve skew.
Quantisation, request batching, autoscaling and right-sizing — typically 40–60% cheaper.
Outcomes
Ranges aggregated across delivered BigAI projects. Specific targets are agreed during the assessment phase.
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| Metric | Before | After | Improvement |
|---|---|---|---|
| Time to get a model into production | 6–10 weeks | 3–5 days | −85% |
| Inference infrastructure cost | Baseline | After optimisation | −40 to −60% |
Case studies
From 14 disconnected sources and manual reconciliation to a single governed lakehouse serving both regulatory reporting and internal analytics.
Read case studyReplacing store-manager intuition with per-SKU forecasting that accounts for seasonality, weather and the promotion calendar.
Read case studyFAQ
A BigAI solution engineer will review your current data estate with you, identify the highest-value problem to solve and sketch a realistic roadmap. No commitment.