The engineering under AI that holds up in production: models served and monitored, costs controlled and quality measured on every change.
Where we help
- Models in production, with rollback
- One gateway to every model provider
- Quality measured, not assumed
- AI features inside your product
Works with
MLflow
Kubeflow
Ray
PyTorch
TensorFlow
NVIDIA
Practices / 02
From experiment
to production.
- 01
MLOps
Training, versioning, deployment and drift monitoring for your models, with a safe rollback.
- 02
LLM gateway
One interface to many model providers, with caching, fallbacks, budgets per team and cost reported by feature.
- 03
Evaluation and observability
Every model call traced, answers scored on your own test sets, and alerts when quality slips.
- 04
Vector search
The right vector store for the workload, with hybrid search and separation between customers.
- 05
Fine-tuning
Models adapted to your vocabulary and format, measured against the base model before they ship.
- 06
AI in your product
Search, summaries, assistants and form filling added to the application you already have.
Stuck between demo and production?
Tell us what works in the demo and what breaks after. We will say what is missing.
AI agents