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PROTOCOL: AGENTIC-AI-ENGINEERING

Agentic AI Engineering & LLM Development

Agentic AI with RAG. 80% fewer hallucinations. Automate 60% of manual tasks.

I build agentic AI systems with RAG pipelines that remember your data. LLM fine-tuning cuts hallucinations 80%. Agentic AI workflows (n8n, LangChain) automate 60% of manual tasks. Production-ready AI agents work like cognitive employees, not chatbots.

The Arsenal

Tools of the Trade

OpenAI API
Claude 3.5
LangChain
Pinecone (Vector DB)
n8n
Python

Legacy Friction

The 'Wrapper' Trap

Generic ChatGPT wrappers don't solve enterprise problems. You need AI that understands your specific data, follows business rules, and takes action.

HiVE Advantage

Cognitive Architecture

I build 'Context-Aware' AI systems with memory and action. Vector DBs (Pinecone) for your company knowledge + n8n for automation = AI that works like an employee, not a chatbot. Result: 5-day AI rebuild vs 3.5 months traditional (Smyl case study).

Execution Pipeline

From Zero to Production.

Data Vectorization

Embedding your knowledge base into Pinecone/Weaviate. Result: AI recalls context with 95%+ accuracy.

Agent Orchestration

Designing multi-step reasoning loops with LangChain. Result: AI completes 10-step workflows autonomously.

Eval & Optimization

Testing against hallucinations using RAGAS framework. Result: 80% reduction in incorrect responses.

ARTIFACTS

What You Get

Custom AI Model (Fine-tuned on your data)
RAG API (Production-ready endpoints)
Automation Workflows (n8n templates)
Eval Dashboard (Hallucination monitoring)

Ready to Deploy?

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