AI startups past POC
You've validated the model, now you need production infrastructure that scales with traffic.
AI & Data Infrastructure
AI & Data Infrastructure
GPU clusters, ML pipelines, data lakes. Purpose-built infrastructure for AI workloads.
What we do
- GPU cluster design (bare-metal or cloud) for training and inference
- Model serving infrastructure with autoscaling
- ML pipeline orchestration (Airflow, Prefect, or custom)
- Data lake setup with S3-compatible object storage
- Feature store and experiment tracking
- Cost-optimized inference with batching and caching
- Compliance-aware data handling (GDPR, SOC 2)
- Monitoring of training runs and inference latency
Stack we use
NVIDIACUDAPyTorchPostgreSQLRedisClickHouseMinIOAirflow
Who this is for
Data teams in growing companies
Your analytics moved beyond Jupyter notebooks. Time for proper pipelines, warehouses, and orchestration.
Teams building with GPUs
Training runs need clusters, inference needs low latency. We design both.