Production-grade LLM infrastructure. We architect, build and deploy intelligent systems that reason, retrieve, and respond — at scale.
Retrieval-augmented generation pipelines that ground LLMs in your private knowledge. Vector stores, chunking strategies, hybrid search, and re-ranking — production-ready.
Autonomous agents with tool-calling, memory, and multi-step reasoning. Built on LangChain and LangGraph for complex orchestration across APIs and data sources.
FastAPI-powered inference endpoints with async streaming, rate limiting, and caching. Deploy any model — GPT-4, Claude, Llama — behind a unified interface.
Extract, classify, and query unstructured documents at scale. PDF parsing, OCR, entity extraction, and semantic Q&A over thousands of documents instantly.
Domain-specific model adaptation using supervised fine-tuning, LoRA, and QLoRA. Align model behavior to your domain vocabulary, tone, and task requirements.
Systematic prompt design, evaluation, and optimization. Chain-of-thought, few-shot, and structured output patterns that maximize reliability across model versions.
Datum Brain ships production-grade LLM systems — not demos. Our stack is Python + FastAPI for AI inference layers, Go for high-throughput backend services, and battle-tested open-source tooling that scales.
We've built document intelligence platforms, autonomous agent workflows, real-time AI runtimes, and multi-model inference APIs. If it involves tokens, embeddings, or reasoning chains — we've shipped it.