Enterprise-grade AI workflows without the cloud-tax

What we build

The pipeline is the product. A model is a small part of the surface area; the ingestion, validation, transformation, embedding, retrieval, monitoring and rollback machinery is what actually decides whether your AI feature survives contact with production data.

• Ingestion and CDC from your real sources - databases, event streams, object stores, SaaS exports - with schema evolution handled, not hoped away
• Transformation and feature pipelines with typed contracts, so a breaking change fails the build instead of corrupting downstream models
• Vector storage and retrieval (pgvector, Qdrant, Weaviate, or managed) sized to your cardinality and latency budget, not to a vendor's demo
• Inference serving with batching, caching, fallbacks and cost guards - so a model swap or a spike does not take down the product
• Observability: data-quality drift, embedding drift, latency, cost-per-request, and a rollback path that is tested, not theoretical

Why mid-sized companies win here

You have real data volume and real compliance constraints, but not a 40-person platform team. We give you the same pipeline discipline an enterprise would pay millions for, scoped to your actual scale, built by seniors, and handed over with the docs your team needs to run it.

Handover, not hostage

Everything we build is documented, tested and yours. When you bring it in-house - and most clients do within 6-12 months - your team can run it on day one. That is the design goal, not a side effect.