Taki: 14 agents over one live web bundle

Most "AI for revenue" is one LLM, one feed. Real enterprise decisions are cross-functional different departments see the same target through different lenses, then a strategist reconciles them through structured frameworks. Taki models that. 14 Gemini 2.5 Pro agents on Vertex AI, wired through a LangGraph cascade over one shared Bright Data live-web bundle (scrape once, the Lean way). Four department agents Marketing, GTM, Finance, Security; read the same scrape in parallel and emit structured, citation-grounded outputs. A cross-pollination LLM writes explicit dept-to-dept handoffs ("Finance → GTM: $1.5B cost-cutting program, sell efficiency"). Then five reasoning agents Strategy, Porter's Five Forces, SWOT, PESTLE, Contradictions run in parallel on the grounded base. Everything assembles into one CascadeBrief. Three hackathon tracks, one cascade: • Track 1 (GTM) → AccountBrief: buying signals, competitor moves, hiring signals, outreach angle. • Track 2 (Finance) → MarketSignal: pricing trend, expansion/contraction, traffic proxy, vendor health. • Track 3 (Security) → RiskProfile: exposure, reputational, regulatory, third-party risk. Bright Data is the only live-web layer. An LLM query-generator emits ~8 industry-specific Google queries; Web Unlocker fetches every SERP page, then every discovered URL 60-75 sources per run. A tier classifier ranks each URL: T1 regulators full weight, T2 academic, T3 news/analyst, T4 trade press, T5 community capped 30%, T6 reviews capped, BLOCKED (Facebook/Instagram/raw blogs) zeroed. A SpendTracker enforces a budget cap so unattended runs can't burn the credit. Five guardrail layers: PII redaction, leak filter with trusted-publisher exemption, claim-level grounding (every claim must cite a snippet in the bundle), citation-level grounding (every URL must map back to a fetched source), and a hallucinated-URL filter on cross-talk. Drops, not flags. Three tracks in one product, not three duct-taped together.