Infynic Autonomous AI Suite
Domain-Anchored Vector Search (RAG) & Multi-Model Autonomous Operations Engine
Problem & System Requirements
Eliminating hallucination and non-deterministic schema errors when executing autonomous LLM workflows over internal enterprise documentation.
Standard LLM prompts produced hallucinated answers when queried on company-specific data.
Unstructured LLM responses broke downstream database insertions and external API webhooks.
Single LLM provider outages halted critical customer-facing automated operations.
How the System Was Engineered
Constructed text-splitter pipeline generating 512-token chunks with 64-token overlap indexed into Pinecone.
Implemented Pydantic schema validation guaranteeing 100% deterministic JSON output from LLM agents.
Built latency-based fallback routing between Anthropic Claude 3.5 Sonnet and OpenAI GPT-4o.
Created secure webhook listener orchestrating multi-step automated database mutations.
Shipped Engineering Components
Document chunking and vector embedding pipeline indexing unstructured PDFs into Pinecone
Domain-anchored Retrieval-Augmented Generation (RAG) system with JSON schema enforcement
Multi-model fallback routing between Anthropic Claude 3.5 Sonnet and OpenAI GPT-4o
Event-driven webhook agent executing multi-step business logic and database writes
Why This Technical Stack & Pattern?
Delivers sub-45ms cosine similarity lookups across high-dimensional vector spaces with enterprise SLA uptime.
Guarantees that autonomous AI agents only emit strictly typed, syntactically valid JSON payloads for zero database insertion errors.
Verified System Benchmarks
Role & Execution Governance
Jyotirmay architected the LangChain vector embedding pipeline, JSON schema enforcement wrappers, multi-model fallback routers, streaming UI, and FastAPI test suites.
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Let's Engineer Your Platform.
Work with Jyotirmay Ray to implement the architecture proven in Infynic Autonomous AI Suite for your platform.