Explore dedicated architectural breakdowns and quantifiable ROI results for enterprise Agentic AI and Data Science systems built by Rohit.
Multi-agent supply chain routing system reducing delay penalties by $1.4M using CrewAI, LangGraph, and Qdrant.
Enterprise medical document search querying 4.5M research papers with 82ms response time and 99.4% precision.
PyTorch deep learning autoencoders analyzing 1.4M transactions per second in 140ms for high-frequency financial compliance.
Private vLLM GPU inference setup replacing $45k/mo API costs with 100% on-premise GDPR data privacy.
Extracted indemnity clauses and renewal dates across 100,000+ corporate legal contracts with 78% faster due diligence.
Multi-agent engineering crew running static security linters and Docker unit tests to accelerate engineering release velocity.
Generating differential-privacy compliant synthetic datasets for medical and financial ML training without exposing customer PII/PHI.
Predicting multi-channel inventory re-order levels across 15,000+ SKUs using PyTorch LSTM neural networks and XGBoost models.
Multi-agent support systems reading Zendesk tickets, querying vector knowledge bases, and executing backend refunds autonomously.
Reducing LLM token spend by 68% using semantic prompt compression algorithms and vLLM prefix context caching on high-volume endpoints.
Architecting multi-tier episodic, semantic, and procedural memory persistence layers for autonomous multi-agent networks.
Deploying fully sovereign, local open-weight AI inference environments operating in zero-internet air-gapped data centers.
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