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orangemantra built our enterprise knowledge base on a RAG architecture with Pinecone and LangChain. The team instrumented evaluation pipelines from day one and hit our recall targets well before the first production release. Document ingestion, chunking strategy, and query routing were all production-grade from the start.

The team integrated GPT-4o into our support workflow with intent classification, context retrieval, and a clean human escalation layer. Guardrails were thorough, latency was far better than our internal prototype, and the evaluation framework they built caught regressions before they reached users.

We needed multi-step agentic workflows to automate internal compliance review. orangemantra built a LangGraph-based agent that handles document retrieval, analysis, flag generation, and case routing with full audit trails. The failure recovery and retry logic held up in production from day one.

orangemantra ran our LoRA fine-tuning pipeline for ICD-10 medical coding classification entirely on-premise. Accuracy moved from 71% with a generic base model to 94% with the fine-tuned version. Training ran on our existing GPU cluster with no data leaving our environment.

The team built a contract analysis pipeline using Claude and a retrieval layer over our historical contract database. Clause extraction, risk flagging, and comparison to standard templates are fully automated. The prompt evaluation framework they built has caught every regression before production.