# OPERATOR IDENTITY SPECIFICATION
OPERATOR:        Suprovo Mallick
CALLSIGN:        DYNO
ROLE:            Generative AI & ML Systems Engineer
ACADEMICS:       B.Tech in Computer Science @ KIIT (2023 - 2027)
ACCREDITATION:   BS in Data Science (Foundation) @ IIT Madras
COORDINATES:     West Bengal, India [88.3639° E, 22.5726° N]

# COMBAT MANIFESTO
DIRECTIVE: >
  "Notebook prototypes are child's play. I engineer verifiable,
  evidence-grounded, low-latency AI pipelines and multi-agent
  swarms architected to withstand heavy production concurrency."

• Adaptive RAG & Dynamic Pruning [████████████████] 100% (NLI Filtering)
• Multi-Agent State Orchestration [██████████████░░] 92% (LangGraph StateGraphs)
• Deterministic Output Grounding [██████████████░░] 90% (Token-Overlap Bypass)
• Inference Concurrency & APIs [███████████████░] 95% (FastAPI / 4-bit Quantization)
• Open Source Runtime Patches [████████████████] 98% (ComfyUI / LlamaIndex / Haystack)

🧬 NODE 01 // ADAPTIVE EVIDENCE RAG

Intelligent, Self-Evaluating Context-Pruning Retrieval Engine
Latency: < 2.0s • Token Pruning: ~80% • Hallucination Rate: < 10% • EM Accuracy: > 75%

[ARCHITECTURE FLOW]
Query ──► Dynamic Expansion ──► 5x Parallel Fetch ──► NLI Pruning (>=0.70) ──► Grounded Qwen-4bit

• Hallucination Suppression ($< 10%$): Employs strict NLI contradiction filtering and atomic claim extraction prior to synthesis.
• Context Sanitation: Cross-encoder NLI independence metric eliminates $\sim 80%$ of redundant tokens, ensuring ultra-dense context windows.
• Full-Stack Container: Containerized React.js + FastAPI pipeline powered by a local 4-bit quantized Qwen model for sub-2-second inference.

🌌 NODE 02 // PLUTO v2

Multi-Agent Document Intelligence & Evidence Verification Mesh
Concurrency: 8-Worker Pool • Extraction Latency Drop: ~80% • Citation Precision: 100%

[MULTI-AGENT PIPELINE]
Document ──► Router Node ──► Extractor + Retriever ──► Synthesizer ──► Critic Gate ──► Verified Payload

• 100% Verifiable Quote Attribution: Router $\to$ Extractor $\to$ Synthesizer $\to$ Critic state graph guarantees unbacked claims are scrubbed.
• 80% Latency Drop: Multi-worker thread pools combined with an internal two-tier LLM response cache eliminate redundant computation.
• Heuristic Bypass Gate: Token-overlap heuristic ($\ge 0.72$) bypasses heavy LLM critic verification for obvious evidence matches.

Autonomous Architecture, Schema & Technical Specification Engine
Engine: LangGraph StateGraph • Inference: Groq LLaMA-3 LPU • Stack: Streamlit + Python

[AUTONOMOUS COMPILATION]
Product Vision ──► Agent Graph ──► Requirements Spec + Architecture Blueprints + DB Schemas + Pseudocode

• Compiles natural-language product ideas into complete, production-grade technical blueprints, database schemas, and executable pseudocode.
• Employs stateful LangGraph agents that autonomously critique technical viability and validate dependency graphs before compilation.

Production AI Agent Architectures, MCP Directory & Developer Rule Blueprints
Architectures: Hierarchical Supervisor • Evaluator-Optimizer • Tooling: MCP Servers • Runtimes: Claude, Cursor, Antigravity

[AGENTIC TOPOLOGY]
Prompt ──► Orchestrator ──► Subagents (Research/Coding) ──► MCP Tool Call ──► Verifier

• Battle-Tested Agent Blueprints: Curated system prompts and .cursorrules, CLAUDE.md, and AGENTS.md templates to eliminate token waste and prevent hallucinations.
• Model Context Protocol (MCP) Integration: Production-ready server configurations for Filesystem, Git, Postgres, and SQLite tool execution.
• Automated Workspace Setup CLI: Includes interactive Python wizard for auto-detecting project stacks and deploying agent directives in seconds.