[01 // DOSSIER] • [02 // ARCHITECTURE] • [03 // SYSTEMS] • [04 // OSS & BADGES] • [05 // ARMORY] • [06 // RESEARCH] • [07 // DSA CORE] • [08 // VAULT] • [09 // TELEMETRY]

01 // NEURAL_DOSSIER [IDENTITY_MATRIX]

# ── OPERATOR SPECIFICATION ────────────────────────────── OPERATOR: Suprovo Mallick CALLSIGN: DYNO DESIGNATION: Generative AI & ML Systems Architect ACADEMICS: B.Tech in Computer Science @ KIIT (2023 – 2027) ACCREDITATION: BS in Data Science (Foundation) @ IIT Madras COORDINATES: West Bengal, India [22.5726° N, 88.3639° E] # ── ENGINEERING MANIFESTO ─────────────────────────────── DIRECTIVE: > "Notebook prototypes are child's play. I engineer verifiable, evidence-grounded, low-latency AI pipelines and multi-agent meshes architected to withstand heavy production concurrency." ⚡ SUBSYSTEM CAPABILITY GAUGES CAPABILITY VECTOR TELEMETRY LOAD CORE MECHANISM Adaptive RAG & Dynamic Pruning ████████████████ 100% Cross-Encoder NLI Filtering (>= 0.70) Open Source Runtime Patching ████████████████ 98% ComfyUI • LlamaIndex • Haystack • Open-WebUI Inference Concurrency & APIs ███████████████░ 95% Async FastAPI • 4-Bit Quantization • Docker Multi-Agent State Orchestration ██████████████░░ 92% Cyclic LangGraph StateGraphs & MCP Deterministic Output Grounding ██████████████░░ 90% Token-Overlap Heuristic Bypass (>= 0.72)

; ── PRODUCTION BENCHMARK TELEMETRY ── [SYSTEM_METRICS] END_TO_END_LATENCY = < 2.0s (Sub-2s) HALLUCINATION_BOUND = < 10.0% CONTEXT_PRUNING = ~80.0% Redundancy Cut CITATION_PRECISION = 100.0% Verifiable UPSTREAM_PR_VELOCITY = 78 Merged PRs

02 // ARCHITECTURAL_CORE [EVIDENCE_FIRST_AI]

"Foundation models are commoditized runtime engines. Real AI engineering begins at query semantics, dynamic NLI pruning, context sanitation, and deterministic output verification."

flowchart LR
    classDef influx fill:#050816,stroke:#00f7ff,stroke-width:2px,color:#00f7ff;
    classDef router fill:#120136,stroke:#7209b7,stroke-width:2px,color:#ffffff;
    classDef filter fill:#240046,stroke:#ff007f,stroke-width:2px,color:#ff70a6;
    classDef mesh fill:#0a192f,stroke:#00ff99,stroke-width:2px,color:#00ff99;
    classDef gate fill:#1f1147,stroke:#ffd166,stroke-width:2px,color:#ffd166;
    classDef out fill:#003566,stroke:#00e5ff,stroke-width:2px,color:#ffffff;

    subgraph S1 ["01 // HYBRID INGESTION"]
        direction TB
        Q["User / Telemetry Influx"]:::influx --> R["Semantic Query Router"]:::router
        R --> S["Hybrid Fetch: ChromaDB + BM25"]:::router
    end

    subgraph S2 ["02 // SANITATION & REASONING"]
        direction TB
        P["Cross-Encoder NLI Pruner (≥ 0.70)"]:::filter --> M["LangGraph Multi-Agent Mesh"]:::mesh
    end

    subgraph S3 ["03 // DETERMINISTIC GATE"]
        direction TB
        V{"Overlap Heuristic ≥ 0.72?"}:::gate
        V -- "Fast-Path Bypass" --> O["Async FastAPI Payload (< 2.0s)"]:::out
        V -- "Ambiguous Claim" --> C["Deterministic Critic Node"]:::filter
        C --> O
    end

    S --> P
    M --> V

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LAYER	ENGINE SUBSYSTEM	DETERMINISTIC MECHANISM	QUANTIFIED IMPACT
L01	Hybrid Vector-Sparse Indexing	Fuses ChromaDB dense embeddings with BM25 lexical rankers across 5x parallel retrieval workers.	High Multi-Modal Recall
L02	Cross-Encoder NLI Pruning	Scores atomic passage entailment (≥ 0.70), stripping redundant & contradictory claims pre-synthesis.	~80% Token Reduction
L03	Stateful Multi-Agent Mesh	LangGraph StateGraph isolating Router → Extractor → Synthesizer → Critic with cyclic recovery.	Zero Cascading Loops
L04	Deterministic Verification Gate	Token-overlap heuristic (≥ 0.72) short-circuits heavy LLM critic calls on verified quote matches.	< 10% Hallucinations
L05	Low-Latency Runtime Delivery	Containerized multi-worker async FastAPI + two-tier response cache + 4-bit quantized inference.	< 2.0s End-to-End

03 // FLAGSHIP_SYSTEMS [PRODUCTION_NODES]

🧬 NODE 01 // ADAPTIVE EVIDENCE RAG Self-Evaluating Context-Pruning Retrieval Engine Latency: < 2.0s • Pruned: ~80% • Hallucination: < 10% • EM: > 75% Query ─► Expansion ─► 5x Fetch ─► NLI (≥0.70) ─► Qwen-4bit Hallucination Suppression (< 10%): Enforces NLI contradiction filtering and atomic claim extraction before synthesis. Context Sanitation: Cross-encoder NLI independence metric strips ~80% of redundant tokens for dense context windows. Full-Stack Container: Containerized React.js + FastAPI pipeline running a local 4-bit quantized Qwen model.

🌌 NODE 02 // PLUTO v2 Multi-Agent Document Intelligence & Verification Mesh Workers: 8-Thread Pool • Latency Drop: ~80% • Citation: 100% Doc ─► Router ─► Extractor+Retriever ─► Synthesizer ─► Critic 100% Verifiable Quote Attribution: Cyclic Router → Extractor → Synthesizer → Critic state graph scrubs unbacked claims. 80% Latency Drop: 8-worker thread pool + internal two-tier LLM response cache eliminates redundant inference. Heuristic Bypass Gate: Token-overlap heuristic (≥ 0.72) bypasses expensive LLM critic calls for direct evidence matches.

🧠 NODE 03 // INTENT COMPILER Autonomous Architecture, Schema & Spec Engine Engine: LangGraph • Inference: Groq LLaMA-3 LPU • UI: Streamlit Vision ─► Agent Graph ─► Specs + Blueprints + Schemas + Code Zero-to-Blueprint Compilation: Transforms natural-language product concepts into full technical specs, DB schemas, and executable pseudocode. Autonomous Viability Critic: Stateful LangGraph agents audit architectural bottlenecks and validate dependency graphs prior to output.

⚡ NODE 04 // AWESOME AGENTIC PATTERNS Production Agent Architectures, MCP Directory & Rulebooks Topologies: Supervisor & Evaluator • Tooling: MCP • IDEs: Claude / Cursor Prompt ─► Orchestrator ─► Subagents ─► MCP Server ─► Verifier Battle-Tested Agent Blueprints: Curated .cursorrules, CLAUDE.md, and AGENTS.md directives to eliminate token waste and drift. Model Context Protocol (MCP) Configs: Ready-to-deploy server setups for Filesystem, Git, Postgres, and SQLite tool execution. Interactive Setup CLI: Python wizard that auto-detects repository stacks and injects agent directives in seconds.

🛰️ AUXILIARY DEPLOYMENT MATRIX

SYSTEM NODE	CORE STACK & ARCHITECTURE	KEY PERFORMANCE METRIC	LIVE ENDPOINT
🌾 WHEAT GUARDIAN	Edge CV Crop Pathology (EfficientNetV2B2 + OpenCV + FastAPI)	93%+ Accuracy • ~300ms Latency	DEPLOYED APP
🌍 TRANSLATE-V2	Low-Resource NMT Transformer (PyTorch + NLLB-200 + FastAPI)	-38% Latency Gain via Batching	HF SPACE
🥗 AAHAR	Dietary RAG Assistant (LangChain + Gemini + ChromaDB + React)	Personalized Multi-Turn Memory	WEB APP
💰 AI EXPENSE ADVISOR	Financial Retrieval Engine (Streamlit + LangChain + Gemini)	Semantic Spend Classification	GITHUB REPO

04 // OPEN_SOURCE_OPS [UPSTREAM_ENGINEERING]

PRODUCTION REPOSITORY	UPSTREAM ENGINEERING IMPACT & PR MISSION	ECOSYSTEM DOMAIN	STATUS
Comfy-Org / ComfyUI (PR #16346 / #16312)	Runtime Reliability: Guarded prompt_worker against unhandled execution exceptions, preventing worker thread crashes.	GenAI Core Engine
open-webui / open-webui (PR #30047 / #30006)	Routing & Observability: Extended parse_custom_headers() to propagate model_id alongside user contexts for downstream routing.	LLM Interface
run-llama / llama_index	Context Integrity: Implemented header-aware deterministic chunking and post-RAG verification primitives.	Agentic RAG
deepset-ai / haystack	Evidence Validation: Engineered factual consistency and hallucination scoring modules against source documents.	Search Pipelines
MakazhanAlpamys / Soup	Distributed Training: Patched DeepSpeed empty-param-group guards to properly forward transformer model arguments.	Model Training
smoothAPI & Surgite	Backend Resilience: Built async retry predicates, backoff logic, and extensible model provider wrappers.	Cloud / APIs

🏆 GITHUB ACHIEVEMENTS & MILESTONE LEDGER

BADGE	MILESTONE	TIER / STATUS	VERIFICATION & EVIDENCE
🧑‍🤝‍🧑 Pair Extraordinaire	Co-authored commits on merged PRs	Silver 🥈 ✅ (Gold 🥇 Ready)	78 Merged PRs (aws-powertools, openinference, anyio, Agronaut + 70 Showcase)
🧠 Galaxy Brain	Accepted answers in technical discussions	Bronze 🥉 ✅ (Silver 🥈 12/16)	12 Verified Accepted Answers (4 away from Silver Tier)
🌟 Starstruck	Repository community star traction	In Flight 🚀	awesome-agentic-patterns (Production AI Agent Playbook)
🦈 Pull Shark	Merged pull requests across public repos	Silver 🥈 ✅	Sustained upstream PR merges across AI & Python infrastructure
🎯 YOLO	Merged pull request without review	Unlocked ✅	Verified repository workflow execution
⚡ Quickdraw	Closed issue or PR within 5 minutes	Unlocked ✅	Verified rapid maintainer triage response

05 // TACTICAL_ARMORY [FULL_SPECTRUM_STACK]

06 // INDUSTRY_LOG [EXPERIENCE_&_RESEARCH]

🧪 ROLE // AI CHATBOT & GENAI BACKEND DEVELOPER FED Society • 2024 — PRESENT Client ─► [FastAPI Gateway] ─► [Vector RAG] ─► [Memory] ─► Output Modular FastAPI Microservices: Engineered scalable backends integrating persistent vector stores with multi-turn session memory. 30% Latency Reduction: Accelerated end-to-end pipelines via embedding matrix pooling and selective cross-encoder reranking. Resilient Inference Endpoints: Built fallback-enabled LLM routing powering high-availability conversational workflows.

📚 PUBLICATION // RAAHI (PIC-2026) Real-Time Transport Tracking in Low-Connectivity Cities Proceedings of the 15th Project Innovation Contest (PIC-2026) Satellite Event of ICDCIT-2026, KIIT status: Peer-Reviewed & Published rank: Top 40 out of 160 global submissions focus: [Distributed Systems, Edge Routing, Telemetry Sync] Formulated a fault-tolerant telemetry synchronization protocol preserving positional continuity across packet-loss-heavy cellular networks.

07 // ALGORITHMIC_CORE [PROBLEM_SOLVING]

ALGORITHMIC DOMAIN DEPTH CORE HEURISTICS & PATTERNS Sliding Window & Two Pointers ████████████████ 96% Dynamic Subarrays, Monotonic Queues, Stream Optimization Trees, Heaps & Tries ██████████████░░ 90% Binary Lifting LCA, Prefix Search Trees, Priority Caching Dynamic Programming & Graphs ██████████████░░ 88% State Compression, Topological Sort, Dijkstra, Memoization Asymptotic & Memory Bounds █████████████░░░ 85% Space-Complexity Guards, Bit Manipulation, Cache Locality

08 // CREDENTIALS_VAULT [CERTIFICATIONS]

ISSUING AUTHORITY	SPECIALIZATION / CREDENTIAL	VERIFICATION
Udemy	AI Engineer Agentic Track: Complete Agent & MCP Architecture
IBM / Coursera	IBM RAG and Agentic AI Professional Specialization
AWS / Coursera	AWS Generative AI Applications Specialization
AWS / Coursera	AWS Fundamentals Specialization
Microsoft / LinkedIn	Career Essentials in Generative AI
IIT Madras	Foundation Level Certificate, BS in Data Science & Applications

09 // TELEMETRY_STREAM [REPO_METRICS]

10 // COMM_LINK [TRANSMISSION_PORT]

; ── OPEN TRANSMISSION PROTOCOL ─────────────────────────
[SYSTEM_TRANSMISSION]
STATUS   = "LISTENING_ON_ALL_PORTS"
CHANNELS = ["Agentic RAG Architectures", "Multi-Agent System Graphs", "Deterministic AI Verification", "High-Throughput ML Backends"]
QUOTE    = "Logic over guesswork. Evidence over hallucinations. Deployed systems over notebooks."