1710+1710+ day streakPrince has solved at least one problem every day on LeetCode5000+5000+ problems solvedAcross 10+ platforms: LeetCode, GFG, CodeStudio, InterviewBitHire
Served as the flagship chat surface of a platform tested with 1000+ active users, processing 18,000+ AI queries from 12,000+ unique visitors and gathering 800+ detailed user feedback that drove 3 major product iterations to a 99% user satisfaction rate - all with no API keys required from users.
Achieved ~99% RAG accuracy and 95% hallucination reduction through the 3-Layer Hybrid RAG pipeline over 10M+ ChromaDB embeddings - Layers 1 and 2 (13 regex intent rules + 12 pre-cached semantic anchors) resolve most queries with zero additional embedding API calls, and full context retrieval completes in <1 second, so every answer is grounded in verifiable production facts instead of generic LLM filler.
Sustained sub-300ms first-token latency across all 35 models over four provider lanes on three clouds - because several frontier models are served on both Azure Anthropic and AWS Bedrock, a degraded lane is bypassed by picking the same model on the other cloud, so a single provider outage never takes ARX offline.
Improved perceived response latency by 60% through adaptive SSE streaming (2–8 char chunks, ±3ms jitter, <50ms intervals) combined with the ChromaDB response memory cache - repeated questions replay instantly from cache while feeling identical to live inference, cutting redundant LLM token spend with zero buffering under high load.
Eliminated model-release lag: with the declarative model catalog and the ActiveAIModels registry, new frontier models (GPT 6.1 Sol, GPT 6 Luna, GPT 6 Astra, Claude Opus 5, Gemini 3.8 Flash, Gemini 3.5 Flash, Grok 4.7) shipped as one spec plus one seed entry each and went live in chat, Versus and voice at once, while retiring a model is a registry flip with zero redeploys - keeping ARX permanently current in a market where model generations turn over monthly.
Reduced API abuse by 99% on infrastructure handling 50K+ daily API calls: every chat request clears JWT auth and Redis-backed rate limiting before any model call is made, so not a single unauthorized request has ever triggered LLM spend - with structured 429 envelopes rendering accurate live countdown timers without any frontend polling.
Kept answers permanently fresh with zero manual content operations: the daily cron re-ingestion pipeline re-embeds all 12 data sections from MongoDB into ChromaDB automatically, so profile, project, and experience updates propagate into RAG answers within 24 hours without anyone touching the pipeline.
Built on the same production AI infrastructure powering systems that reached 600K+ users, drive 80%+ of platform traffic, and run a <40ms AI inference review engine - ARX is a customer-facing window into battle-tested architecture, not a demo assembled for screenshots.
Generated direct inbound opportunity: multiple job calls and offers from AI companies, and 3–4 founders reached out to build similar multi-model chat products after using ARX and seeing the architecture respond in real time - the product itself became the strongest proof of engineering capability.
Made unauthorized model spend structurally impossible rather than merely unlikely: because JWT verification and quota checks both resolve before the controller is entered, there is no code path in which an unauthenticated or over-quota request reaches a provider API - and the 4-layer perimeter rejects a direct curl for a missing Origin header before authentication is even attempted, closing the exact gap that CORS alone structurally cannot cover.
Cut retrieval cost to zero LLM calls per query by moving query expansion into the corpus at index time instead of paying for query rewriting on every request - where a conventional RAG pipeline spends multiple LLM calls per query on rewriting and HyDE, this one spends none, and intent routing resolves the most common questions with no embedding call at all, turning them into pure metadata lookups while the remaining layers absorb everything else.
Architected ARX (AI Architect) - a standalone, product-grade conversational AI application at /arx built on Next.js 15 (App Router), React 19, TypeScript 5, and Tailwind CSS 4 - giving direct chat access to 35 frontier models across 11 providers including OpenAI (GPT 6.1 Sol, GPT 6 Luna, GPT 6 Astra, GPT-5.4 Pro, GPT-5.3 Codex, GPT-5.4 Mini), Anthropic (Claude Opus 5, Opus 4.8, Opus 4.7, Opus 4.6, Opus 4.5, Opus 4.1, Sonnet 4.6, Sonnet 4.5), Google (Gemini 3.8 Flash, Gemini 3.5 Flash, Gemini 3.1 Pro, Gemini 3 Flash, Gemma 3 27B), xAI (Grok 4.7, Grok 4.3, Grok 4.2 Reasoning, Grok 4.2 Non-Reasoning), DeepSeek (V4 Pro, V4 Flash), Meta (LLaMA 4 Maverick), Mistral (Mistral Large 3), Moonshot (Kimi K2.6, K2.5), Qwen (Qwen3 VL), Nvidia (Nemotron Nano), and MiniMax (MiniMax M2) - delivered over four provider lanes across 3 clouds - Azure Foundry, Azure Anthropic, Google Gemini and AWS Bedrock through one provider-aware streaming endpoint with sub-300ms first-token latency.
Engineered the full request pipeline surfaced to users as a 6-stage animated workflow: Your Query → JWT Auth & Rate-Limit → RAG Retrieval (ChromaDB vector search pulling full personal context in <1s) → Model Router (picks the best of 35 frontier models) → AI Inference → Token Streaming - every stage backed by the real production backend: Passport.js JWT validation, Redis-backed rate limiting with structured 429 rateLimit envelopes (limit, remaining, resetInSeconds) that the frontend converts into live countdown timers without polling, and SSE token streaming rendered incrementally in the chat UI.
Grounded every answer through a 3-Layer Hybrid RAG pipeline over 10M+ ChromaDB embeddings (Azure OpenAI text-embedding-3-large on self-hosted Azure VMs): Layer 1 - 13 regex intent-detection rules for zero-latency section routing; Layer 2 - 12 pre-cached semantic anchor embeddings compared via cosine similarity with no extra API calls; Layer 3 - full-corpus similarity search fallback; combined with entity-diverse re-ranking (guarantees ≥1 document per company/project) and keyword priority boosts (critical=+3, high=+1) - achieving ~99% RAG accuracy and 95% hallucination reduction so ARX answers about real experience, real architecture decisions, and real production numbers instead of generic LLM output.
Built a four-lane model delivery layer: Azure Foundry serves GPT, Grok, DeepSeek, LLaMA, Mistral and Kimi through one OpenAI-compatible v1 client with the deployment chosen per request; Azure Anthropic serves Claude Opus 5, Opus 4.8/4.7/4.6/4.5 and Sonnet 4.6 through one shared Anthropic Foundry SDK client; Google Gemini serves Gemini 3.8 Flash, 3.5 Flash, 3.1 Pro and 3 Flash through Google GenAI; and AWS Bedrock (ConverseStreamCommand) serves the Bedrock-namespaced Claude models plus Qwen3 VL, Gemma 3, MiniMax M2, Nemotron Nano and Kimi K2.5 - with a MongoDB ActiveAIModels registry carrying a required provider field so a model can be enabled, reordered or disabled at runtime with zero redeploys; the frontend picker hydrates from this registry via a Zustand aiModelsStore, groups models by provider lane, and lets users switch between any of the 35 models mid-session without losing conversation context.
Delivered an adaptive token streaming architecture over Server-Sent Events with dynamic chunk sizing (2–8 characters per write) and ±3ms jitter timing to prevent synchronization artifacts across providers with wildly different native token cadences, sustaining <50ms chunk intervals uniformly across all 35 models - complemented by a ChromaDB-backed response memory cache that re-streams previously computed answers chunk-by-chunk so cached responses feel identical to live inference, eliminating redundant LLM API calls for repeated questions and improving perceived latency by 60%.
Developed an MCP-compliant prompt engineering layer with dynamic system/user role injection and adaptive tone control (professional, casual, friendly, technical) so the same grounded facts render in the register the user asked for, plus Redis-backed long-term session memory preserving conversational context across messages and reconnects; coupled with a daily cron-driven RAG re-ingestion pipeline that automatically clears and re-embeds resume/project data from MongoDB into ChromaDB across 12 structured sections (salary, experience, projects, education, GitHub, mentorship, DSA, skills, achievements, social media, personal, major contributions) - ARX never answers from stale context with zero manual content operations.
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LLMsFrontier models routed per request: OpenAI, Anthropic, Google, xAI, Meta and more.One question, from your browser to the model cloud and back. Click any step to see what runs there and where it lives in the code.