14 Production AI Modules

Improves grounded enterprise knowledge retrieval and reduces unsupported AI answers in operational workflows

Retrieval-augmented generation with Transformers.js embeddings (all-MiniLM-L6-v2) over an in-memory vector index — runs entirely in your browser.

Balances quality, latency, and spend across model tiers for production AI request routing

Multi-model routing across fast and large Groq-hosted models — see live latency, cost, and quality trade-offs per request.

Improves release confidence through measurable quality gates and regression visibility before deployment

Evaluation pipeline walkthrough — deterministic rubric scoring (required-coverage and forbidden-topic checks), guardrail screening, and pass/fail release gating. Deterministic eval suites run in CI on every change.

Coordinates specialized agent workflows with approvals and auditability for high-impact enterprise decisions

Three specialized agents — Analyzer, Researcher, and Strategist — with real LLM calls via Groq, handing off in sequence; the Strategist's recommendation is released only through a server-recorded, single-use approval.

Improves reliability by standardizing tool access across agent workflows

MCP-style tool use with per-tool authorization — watch an LLM select and call JSON-Schema-defined tools (via Groq function calling); calls without the required scope are denied and tool outputs are screened for injected instructions.

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 (deterministic seeded data).

Redacts PII on the device before any cloud call, so the cloud model only receives the sanitized payload

Three-tier privacy-first AI pipeline: BERT NER redacts PII in the browser via Transformers.js ONNX, the server re-checks the payload and holds it behind a single-use approval, and Groq produces an executive summary from the approved payload only.

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.

Demonstrates safe generative UI for production: eliminates unvalidated AI-generated markup as an attack surface while keeping responses dynamic

Ask about Prasad's background — the model can only emit JSON for a fixed component catalog, validated server-side before anything renders.

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.

Reduces infrastructure overhead through smaller, faster production models

Live ONNX benchmark comparing INT8 vs FP32 inference — real file sizes, real latency, real quality diff.

Accelerates strategic research by orchestrating multiple AI expert perspectives into a single structured executive report

Multi-perspective AI research inspired by Stanford STORM — discover expert lenses, generate questions, research each angle, and synthesize an executive brief in real time.

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.