AI Platform Modules
16 Production AI Modules
Every module runs on a shared governance foundation — guardrails, rate limiting, observability, eval-gated CI, and drift monitoring. Each is tied to a real enterprise platform concern: quality, reliability, cost, retrieval, orchestration, or oversight.
Governance layer
Shared
Rate limiting
All routes
Eval gating
CI-enforced
Audit trail
Trace IDs
Core AI Infrastructure
Foundation systems — quality, retrieval, routing, and governance
RAG Pipeline
Improves grounded enterprise knowledge retrieval and reduces unsupported AI answers in operational workflows
Real retrieval-augmented generation with Transformers.js embeddings and ChromaDB — runs entirely in your browser.
What this proves
Shows how enterprise knowledge can be retrieved with source traceability, relevance controls, and citation — not hallucination.
LLM Router
Balances quality, latency, and spend across model tiers for production AI request routing
Real multi-model routing across Llama 3.1 8B, 70B, and Mixtral — see live latency, cost, and quality trade-offs.
What this proves
Routes each AI request to the optimal model — from fast WASM models for simple tasks to Qwen3.6-27B for agentic reasoning — demonstrating LLM FinOps and decision intelligence at the platform level.
Vector Search
Enables semantic discovery and natural-language retrieval across enterprise content systems
Semantic search with real sentence-BERT embeddings and UMAP visualisation of the embedding space.
What this proves
High-throughput semantic search over large corpora with real-time filtering — the retrieval layer for any serious AI product.
Native Browser AI Skill
0ms Latency and 100% Privacy (Edge-inference) for accessibility auditing workflows
A reusable Chrome AI Skill that audits webpage accessibility using on-device Gemini Nano.
What this proves
On-device AI inference with zero server dependency — the architecture pattern for compliance-sensitive enterprise tooling.
Agentic Systems
Autonomous agents, tool-use orchestration, and enterprise control
Multi-Agent System
Coordinates specialized agent workflows with approvals and auditability for high-impact enterprise decisions
CrewAI-powered agents with real LLM calls via Groq — Analyzer, Researcher, and Strategist collaborating in real time.
What this proves
Demonstrates governed agentic workflows with human-in-the-loop approval checkpoints, audit trails, and role-based orchestration — safe for enterprise deployment.
MCP Tool Demo
Improves reliability by standardizing tool access across agent workflows
Model Context Protocol in action — watch an LLM discover and call tools to answer questions about Prasad's background.
What this proves
Shows how standardized tool protocols reduce integration overhead and make agent capabilities composable across enterprise systems.
Enterprise Control Plane
Operationalizes enterprise AI oversight with RBAC, spend controls, and traceable policy enforcement
Org-wide AI governance dashboard — RBAC, group spend limits with token-cost tracking, and structured observability feed.
What this proves
Operational guardrails for enterprise AI: RBAC, spend analytics, token budgets, and structured observability in a single control surface.
Edge Agent + Cloud Agent Collaboration
Enforces data residency at the device boundary while delivering cloud-quality reasoning — zero PII exposure to external APIs
Three-tier privacy-first AI pipeline: BERT NER redacts PII in the browser via Transformers.js ONNX, a HITL gate governs the handoff, and Groq produces an executive summary from the sanitized payload only.
What this proves
Demonstrates the governance-first agentic handoff pattern enterprises need for regulated AI workflows: edge extraction, explicit HITL approval, and auditable cloud orchestration.
Agent Auth Demo
Eliminates bespoke agent onboarding by implementing the emerging open standard for agent identity
Live auth.md protocol implementation — AI agents register anonymously, claim with email + OTP, then call MCP tools with a Bearer credential.
What this proves
Demonstrates agent-native authentication: anonymous registration, email claim, OTP upgrade, and authenticated tool access — the identity primitive every agentic platform needs.
Real-Time Spatial AI + World Modeling Engine
Accelerates logistics and spatial planning with policy-aware world artifacts that are explainable, reviewable, and simulation-ready
Perception → reconstruction → agent reasoning. Precomputed 3D mesh playback with drift correction visualization and LLM spatial query layer. Controllable parametric spatial design — refine generated scenes with natural-language instructions. Changes are validated, diffed, and auditable.
What this proves
Brings LLM reasoning into spatial and operational planning — policy-aware world models that are auditable, diffable, and simulation-ready.
AI Applications
Production AI experiences across modalities
AI Portfolio Assistant
Cuts expert lookup time by making organizational knowledge instantly accessible
Streaming full-context assistant over my experience with optional retrieval-enhanced grounding and cited context cues.
What this proves
Demonstrates conversational AI grounded in structured knowledge — RAG + LLM working together on a real corpus.
AI Hiring Intelligence
Reduces recruiting cycle time through faster candidate-role alignment
Paste a job description — get multi-dimension fit scoring, HITL-gated tailoring, and an ATS-optimized resume with drift detection.
What this proves
AI-powered resume tailoring at scale — shows LLM orchestration applied to a high-frequency, measurable business workflow.
Multimodal Assistant
Lowers processing costs by running vision workflows closer to users
Florence-2 image captioning and OCR running in-browser via Transformers.js — no server, no API key.
What this proves
Edge-deployed vision AI with zero server cost — the architecture pattern for privacy-sensitive document and image workflows.
Model Quantization
Reduces infrastructure overhead through smaller, faster production models
Live ONNX benchmark comparing INT8 vs FP32 inference — real file sizes, real latency, real quality diff.
What this proves
Demonstrates 4-bit MoE quantization delivering 70%+ memory reduction with minimal quality loss — the cost lever most teams overlook.