AI / ML
LLM apps and ML pipelines that ship.
From RAG systems to custom model training — we build AI features that actually work in production, not just on demos.
What we deliver
Concrete outputs, no fluff.
- RAG pipelines with vector databases (Pinecone, Weaviate, pgvector)
- LLM agents with tool use & memory
- Custom fine-tuning and evaluation frameworks
- Computer vision: detection, classification, OCR
- MLOps: training pipelines, model registry, monitoring
- Cost-efficient inference architecture
Tech we use
Battle-tested stack.
OpenAIAnthropicLangChainLlamaIndexPyTorchHugging FacePineconeModal
How we engage
Pick the model that fits.
Discovery sprint
2 weeks to validate feasibility with a working prototype.
Production build
8–16 weeks from prototype to scaled production system.
FAQ
Common questions.
Will my data train the model?+
No. We use enterprise APIs with no-training agreements, or self-hosted models when data sensitivity demands it.
How do you measure if the AI is good enough?+
We build evaluation harnesses with held-out test sets and human-rated samples — not vibes.