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Infynic Autonomous AI Suite

Domain-Anchored Vector Search (RAG) & Multi-Model Autonomous Operations Engine

Internal digital product designed, engineered and operated by Jyotirmay Ray.
ROLE / CAPACITY Founder & AI Systems Architect
PROJECT NATURE Self-Owned Product
TIMELINE / STATUS 2024–Present
RELATED COMMERCIAL SERVICE AI Specs →
01 THE ARCHITECTURAL CHALLENGE

Problem & System Requirements

Eliminating hallucination and non-deterministic schema errors when executing autonomous LLM workflows over internal enterprise documentation.

BOTTLENECK 01

Standard LLM prompts produced hallucinated answers when queried on company-specific data.

BOTTLENECK 02

Unstructured LLM responses broke downstream database insertions and external API webhooks.

BOTTLENECK 03

Single LLM provider outages halted critical customer-facing automated operations.

02 METHODOLOGY & EXECUTION

How the System Was Engineered

01 Chunking & Embeddings

Constructed text-splitter pipeline generating 512-token chunks with 64-token overlap indexed into Pinecone.

02 Strict Schema Validation

Implemented Pydantic schema validation guaranteeing 100% deterministic JSON output from LLM agents.

03 Multi-Model Fallback

Built latency-based fallback routing between Anthropic Claude 3.5 Sonnet and OpenAI GPT-4o.

04 Event-Driven Agent Triggers

Created secure webhook listener orchestrating multi-step automated database mutations.

03 CORE DELIVERABLES

Shipped Engineering Components

01

Document chunking and vector embedding pipeline indexing unstructured PDFs into Pinecone

02

Domain-anchored Retrieval-Augmented Generation (RAG) system with JSON schema enforcement

03

Multi-model fallback routing between Anthropic Claude 3.5 Sonnet and OpenAI GPT-4o

04

Event-driven webhook agent executing multi-step business logic and database writes

04 ARCHITECTURAL RATIONALE

Why This Technical Stack & Pattern?

Why Pinecone Vector Search?

Delivers sub-45ms cosine similarity lookups across high-dimensional vector spaces with enterprise SLA uptime.

Why Pydantic JSON Enforcement?

Guarantees that autonomous AI agents only emit strictly typed, syntactically valid JSON payloads for zero database insertion errors.

05 OPERATIONAL METRICS & PROOF

Verified System Benchmarks

Hallucination Rate < 0.05%
Vector Query Time < 45ms
Automation Throughput 10k+ Tasks/Day
Output Schema 100% Deterministic
FOUNDER LEADERSHIP & TEAM CONTEXT

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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