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Services / AI & Intelligence
Production-grade LLM systems with RAG, hybrid search retrieval and agentic reasoning — not demos that die in staging.
We design and ship LLM systems that survive contact with production: retrieval-augmented generation with hybrid search, structured evals, guardrails and observability from day one. From internal copilots to fully agentic backends orchestrated with LangChain and LangGraph, we treat LLMs as engineering systems — measured, versioned and monitored like any other critical service.
OpenAIAnthropic ClaudeLangChainLangGraphPineconepgvectorElasticsearchCohere Rerank
[A]What we deliver
F/01
RAG Pipelines
Chunking strategies, embeddings, reranking and citation-grounded answers over your private knowledge.
F/02
Hybrid Search Retrieval
BM25 keyword + dense vector search fused with rerankers for precision that pure vector search cannot reach.
F/03
Agentic Workflows
Multi-step, tool-using agents built on LangGraph with state, retries and human-in-the-loop checkpoints.
F/04
Evals & Guardrails
Regression eval suites, hallucination checks and output validation gating every release.
F/05
Fine-tuning & Prompt Systems
Model selection, fine-tuning and versioned prompt pipelines tuned against real usage data.
F/06
LLM Observability
Tracing, token cost tracking and latency budgets so you always know what the system is doing and spending.
Next step
Need this done properly?
Tell us where you are and where it hurts — we will map the shortest safe path to production.
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