Autonomous AI Agentic Systems & LLM Engineering
Deploy enterprise multi-agent orchestration frameworks (LangChain, AutoGen, CrewAI), domain-specific LLM fine-tuning (Llama 3.1, Mistral), and deterministic AI safety guardrails.
Multi-Agent Orchestration Frameworks
Autonomous multi-agent networks operating on LangChain and AutoGen, capable of executing complex multi-step reasoning workflows and tool invocations.
Domain-Specific Enterprise LLM Fine-Tuning
LoRA/QLoRA parameter-efficient fine-tuning on open-weights foundation models (Llama 3.1 70B, Mistral Large) deployed on private cloud GPUs.
Sub-50ms RAG Vector Lakehouses
Hybrid dense-sparse vector indexing (Pinecone, Qdrant, Milvus) with Cohere Rerank v3 for instant, accurate grounding on proprietary enterprise data.
Deterministic AI Safety & NeMo Guardrails
Programmable safety rails preventing prompt injections, PII leaks, and hallucinations, backed by continuous G-Eval accuracy scoring.
AI Agent System Technical Specifications
| AI Architecture Tier | Technology Framework | Response Benchmark | Accuracy & Safety Gate |
|---|---|---|---|
| Agentic Orchestration | LangChain / AutoGen / CrewAI | Parallel Execution | Deterministic Tool Call Validation |
| RAG Vector Indexing | Pinecone / Qdrant / Cohere Rerank | < 45 ms Retrieval | Hybrid Dense-Sparse Grounding |
| LLM Fine-Tuning | Llama 3.1 70B / QLoRA / vLLM | 120 Tokens / sec | NeMo Hallucination Rails (< 0.1%) |
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