Use cases, agents,
and the stack behind them.

Batch LLM processing across 9,300 SKUs — staged apply/rollback with per-batch locks. Runs nightly, unattended.

Buyer Q&A in plain language. Worked around the commerce engine stripping JS via a server-level injection layer.

Location-aware recommendation engine — surfaces parts relevant to a regional market from a sprawling catalog.

Financial-terminal-style demand dashboard with live heatmaps across 15 regional markets + LLM demand reports on request.

Bulk image-generation pipeline with dual-model A/B (Gemini vs GPT Image) and live cost tracking per batch.

Payments, logistics, transactional email, order messaging, and government-API vendor verification wired as one.

Linux admin, CDN/WAF, automated nightly backups, and a written ops runbook. The rules are the real product.

Triangulates GST annual turnover, 12-month bank credits, and ITR declared income. Applies underwriting scoring logic and generates a structured credit narrative with risk flags and recommended limit range. Runs in under 10 seconds.

8 market agents (LCC Revenue Manager, Legacy Network Carrier, ULCC Revenue Manager, Premium Boutique, Enterprise Travel Manager, Price-Elastic Consumer, Online Travel Agency, Miles Optimizer) each receive live fare data, a cascaded P&L shock output, and their own behavioral profile. Each produces an independent fare vote and confidence score. Votes are market-share-weighted (Legacy 42%, LCC 28%, ULCC 12%, Premium 7%) into a consensus predicted fare with a ±band. Stack multiple shock triggers for compounded scenarios. A/B comparison mode for two concurrent runs. Rate-limited Flask API (5 req/60s). Simulation history in localStorage. PDF export. Open-source on GitHub.

Breadth-first search across 13 sectors and 31 directional edges. Each trigger fires first-order shocks; the BFS engine propagates them sector-by-sector with sector-specific lag, confidence, and circuit-breaker thresholds. Edge weights are data-calibrated — not authored — via event-conditional correlation analysis across 23 historical shock events using Yahoo Finance ETF return data. VIX is applied as a real-time cascade multiplier: same WAR_CONFLICT trigger produces different sector numbers at VIX 14 vs VIX 38. Already-priced-in dampening prevents double-counting sectors the market is already moving on today.

Pulls BBC World, BBC Business, NYT World, and NYT Business RSS feeds every 15 minutes. Keyword-matches each headline to 6 shock categories (WAR_CONFLICT, OIL_SHOCK, RATE_HIKE, PANDEMIC, SUPPLY_CHAIN, MARKET_CRASH). Returns signal strength 0–1 and a real headline per trigger category — so every trigger card shows real news relevance, not a static label. ACTIVE badge fires when signal strength crosses threshold.

Receives the full 13-sector cascade output (all sectors, with impact %, lag, confidence, and propagation mechanism) plus live market data. Google Search Grounding fires at inference time — Gemini searches the current web before writing, not its training data. The system instruction requires the model to cite at least two specific cascade sectors by percentage AND anchor each claim to a specific current real-world event. Output: 4-section plain-English brief, under 500 words, specific to one of 11 decision-maker personas. Not a generic macro summary — a personalised analytical report.

Takes six FNOL parameters — policy type, incident category, claim quantum, filing delay, prior claims history, and policy age — and runs them through a multi-signal fraud scoring engine. Outputs fraud risk (LOW to CRITICAL), coverage likelihood, triage priority (FAST_TRACK / STANDARD / INVESTIGATE / REFER_SIU), indicative settlement range, specific risk flags, and a three-paragraph adjuster narrative with recommended next action. Runs in under 10 seconds. No API calls — all inference is in-browser via a rule-based scoring engine calibrated against standard insurance underwriting heuristics.