Enablement Radar

A GTM operations console for AI product portfolios: track the freshness of the public documentation field teams depend on, monitor competitive pricing moves across AI labs and vendors, and assemble evidence-based action memos. Demo dataset: the Google AI ecosystem and its competitive set, July 2026 — all figures from public, cited sources; nothing estimated or invented.

Asset Health Scorecard

Freshness and utilization tracking for customer-facing enablement assets
Asset Last updated Freshness Utilization Why it matters commercially
Freshness thresholds (illustrative rubric — days-since-update is a proxy; a production version would use event-driven staleness tied to dependency changes): ≤ 30 days 31–60 days > 60 days Unknown — no update date published; not scored
Utilization column: intentionally unpopulated. Measuring it requires internal instrumentation — asset views/shares, field-team pulls, attach rate to opportunities — not available from public data. Shown as N/A rather than fabricated.

Data discipline: every row is a real, public asset. Commercial-relevance notes are drawn from the underlying strategy analysis. No dates, scores, or figures are estimated — assets without a published update date are marked "unknown" rather than guessed.

Competitive Gap Monitor

Enterprise and prosumer pricing structures across AI labs and vendors — publicly cited figures only
Gap

Google Stitch has no pricing structure yet, while every comp across the AI labs does. Figma — the direct comp — runs a proven seat-plus-shared-credit hybrid; Anthropic and Mistral sell seat-plus-usage; OpenAI and Perplexity hold flat per-seat structures; Runway, Notion, and Heptabase run metered credits; Google's own Agent Platform meters consumption. Stitch remains free with no paid tier and no payment method required, its commercial architecture unwritten ahead of an expected Q4 2026 pricing decision. This is the kind of open gap this panel exists to surface.

Product Structure Pricing (as cited)
Structures: Seat + credit hybrid Flat per-seat Metered credits Per-license subscription No paid tier

Data discipline: every figure is publicly sourced and cited. Analyst expectations and third-party-tracker figures are labeled as such and never presented as vendor-confirmed. Where a price is not officially published (NotebookLM Enterprise per-license), none is stated.

Action Memo Generator

Turns the evidence in Panels 1–2 into a sequenced, copyable action memo
Copied The generator sequences actions already validated in the underlying strategy analysis — it deliberately does not author new ones, so no unverified claim can enter at the recommendation step.

Data discipline: the memo surfaces pre-validated actions sequenced Now / Next / Later. This panel introduces no new claims.

Prototype scope & limitations — read before evaluating

1. Detection is manual in this prototype. The "change detected" flag records a change hand-sourced from the vendor's own documentation; no crawler or diffing pipeline runs behind it. The prototype demonstrates the post-detection workflow — diff, urgency flag, affected products, documented upsell path, memo. The production design is a watcher over a registry of tracked assets, diffing snapshots and routing structured alerts, with announcement lead time (here, 27 days) as the key signal.

2. Utilization is not measured. Asset health is freshness and utilization; utilization requires internal instrumentation (asset views/shares, field pulls, opportunity attach rate) unavailable from public data. It is shown as N/A rather than fabricated. Freshness thresholds (30/60 days) are an illustrative rubric; a production version replaces calendar age with event-driven staleness tied to dependency changes.

3. The memo generator sequences; it does not author. Actions are drawn verbatim from the underlying strategy analysis. Asset and competitor selection reflects that analysis's target gap; a production scorecard would run on a jointly owned asset registry, not a hand-picked list.

Independent prototype built entirely on public, cited sources. Demo dataset references Google, OpenAI, Perplexity, Figma, Notion, and Heptabase products. Not affiliated with or endorsed by any vendor referenced.