Getting a model to work in a demo is easy. Getting it to work reliably in production — at scale, for years — is the hard part. We build the infrastructure that closes that gap.
Talk to Our Team✓ model training — complete
✓ evaluation — accuracy: 94.2%
✓ drift check — baseline stable
→ deploying to staging...
✓ canary deployment — 5% traffic
→ monitoring LLM cost: $0.0032/req
✓ guardrails — all checks passed
→ promoting to production...
Traditional ML models in production
Large language models & generative AI
The most common ways AI projects fail in production — and how we address each one.
✗ Model drift goes undetected for months
✓ Automated drift detection with configurable alerting thresholds
✗ No retraining pipeline when performance degrades
✓ Triggered retraining pipelines tied to evaluation metrics
✗ LLM prompt changes break downstream systems
✓ Prompt versioning, regression testing, and staged rollouts
✗ No audit trail for model decisions
✓ Full logging and decision traceability for regulated use cases
✗ Token costs spiral without visibility
✓ Real-time cost dashboards and automated budget controls
✗ Deployment is manual, slow, and error-prone
✓ Fully automated CI/CD with automated rollback capability
Tool-agnostic. We work with your existing stack where possible.
Talk to us about building the infrastructure that makes your AI investment last.
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