LLM agents, retrieval pipelines, and eval harnesses.
We build agentic architectures and RAG systems that hold up in production — not just in a demo. Rigorous evaluation, clean ops, and measurable ROI for complex workloads.
Core Capabilities
Agentic architectures
Autonomous LLM agents with multi-step reasoning, tool use (function calling), and state management. We orchestrate complex workflows that go far beyond simple prompts.
Retrieval & embeddings
Vector databases (Pinecone, Qdrant), hybrid search (BM25 + dense), and chunking strategies tuned for context precision in RAG pipelines.
Evaluation & guardrails
Systematic LLM output testing. Ragas metrics, LLM-as-a-judge setups, and strict output validation (Zod/Pydantic) to minimize hallucinations.
LLM ops in production
Observability for prompts, agent-step tracing (Langfuse/Arize), and latency optimization. Systems that can be monitored and scaled under load.
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