Product Intelligence Platform
Turn product intelligence into product strategy
See how the world’s best product teams actually operate — what they ship, how they price, how they position, who runs product, the internal tools they build, and how far their AI adoption has actually gone — so you can build a stronger strategy of your own.
The platform
20,000+ product signals, read and judged so you only see what matters
Signals
Track launches, feature releases, internal tooling, hiring, and positioning shifts in one place.
20,000+ signals judged · 6,500 activeShowing 1–6 of 3,317
Tracking product excellence at world-leading teams
What you can do with it
Product excellence at your fingertips
FigmaShipped an agent that turns a design file into a prototypeLaunch2h ago
LinearInitiatives now support team ownership and private visibilityFeature18h ago
StripeMoved code review onto an internal coding agentInternal tool1d ago
AnthropicReleased a paper on measuring model introspectionResearch1d ago
RampOpened 8 ML engineering roles across two new officesOrg3d ago
Put real innovation on your roadmap
Know what the world’s leading product-led companies are actually shipping. Agents and the harnesses around them, new model releases, MCP integrations, voice interfaces, retrieval and eval tooling — alongside the pricing moves, internal tooling and hiring that show what a team is really betting on.
Spot an emerging technology while it is still a decision, not after it is table stakes.
Strategy Thought Canvas
Work a decision through on one canvas
Put the question in the middle and build the case around it: evidence from Product Intelligence on one side, the realistic options below, and a framework run over them on the other. A Next step guide takes you from question to decision, and the finished canvas becomes a deck or a document to share.
Evidence · UI example
AI add-on
A footnote clarifies that certain AI features like Coding sessions and Loops require AI credits, revealing metered usage hidden behind a flat per-seat price.
Linear pricingTeardown
Evidence · Signal
Pay-per-use model will become standard offering for AI agents, with upgrade paths
Perspective from Jeff Weinstein, Senior Product Manager.
Stripe · 18 Aug 2026Open
Evidence · Pricing teardown
Metronome AI pricing
Pricing tiers: Starter (unpriced: Billing volume billed at 0.8%, Events volume billed at $0.04/1k ingest events, Real-time usage metering and alerting…
Metronome · 14 Aug 2026Open
Evidence · Pricing teardown
LaunchDarkly pricing
LaunchDarkly uses tiered plans layered with usage-based pricing; it starts at $10 metered by usage, offers a free tier, and includes a full feature comparison table.
LaunchDarkly · 1 Oct 2026Open
The question
Should we move our AI features to usage-based pricing?
Option A
Keep flat subscription, fence AI with soft fair-use caps
Leave the per-seat plan as it is. Add a published fair-use ceiling on the heaviest AI features and a clear footnote, similar to the credits footnote in Linear’s teardown. Only accounts that exceed the ceiling get a conversation about an add-on.
- Upside
- Keeps the buying decision simple while protecting you from a few heavy users eating margin.
- Risk
- Caps feel arbitrary, and you may give up revenue from your most engaged accounts, who would pay more.
- Watch
- Share of accounts that hit the ceiling in a quarter, and whether they upgrade, grumble or churn.
Option B
Hybrid: seat platform fee plus prepaid AI credit packs
Keep the annual subscription as a platform fee with a bundled monthly credit allowance. Heavy AI actions draw down credits, and teams buy top-up packs when they run out. This follows Siena’s platform fee, LaunchDarkly’s tiers layered with metering, and Linear’s credits footnote.
- Upside
- Revenue scales with real AI cost and value while the base price stays predictable for procurement.
- Risk
- Credits add friction and bill-shock anxiety for enterprise buyers, and metering has to be built before other work they ask for.
- Watch
- Whether prospects ask about credit limits and overage before features, and the share of accounts buying top-ups.
Option C
Don’t move yet; price on seats and learn from usage
Hold pricing steady while usage data is thin. Instrument per-account AI usage and cost behind the scenes, and use the next round of demos and early renewals to see how teams actually use it.
- Upside
- Avoids locking in a model before you know which AI features customers value.
- Risk
- If a few accounts run up heavy AI costs, margin erodes quietly, and Stripe’s view that pay-per-use becomes standard could force a later price change.
- Watch
- AI cost per account as a share of its subscription, and any prospect asking for usage-based terms unprompted.
Framework · Pre-mortem
Option C ahead, with usage instrumented now so Option A or B can follow once demo and renewal data exist
- Option A: a year on, it failed because the caps were guesses. With no usage data, the ceiling was set too low and annoyed the best accounts, or too high and protected nothing. Linear’s credits footnote shows a fence can work, but Linear knows its cost curve.
- Option B: a year on, it failed because credits slowed the sales cycle. The evidence for B is real (Siena’s platform fee, LaunchDarkly’s tiers layered with metering, Stripe’s view that pay-per-use becomes standard for AI), but it shows where pricing is heading, not that these buyers want it today.
- Option C: a year on, it failed because one or two heavy accounts quietly ate margin. A real risk, but bounded and watchable: track AI cost per account as a share of subscription and set a trigger.
- Conclusion: Option C comes out ahead. A and B fail from acting on missing information; C fails only if you do nothing with the data. Treat it as a short, dated decision, not a default.
Next step
See what the market is doing
Adds the five most relevant signals, decisions and teardowns from Product Intelligence.
Drag to pan · ⌘/Ctrl + scroll to zoom · Shift-click to select several cards
One canvas for “Should we move our AI features to usage-based pricing?”: the evidence it found, the three options it laid out, and a pre-mortem that weighs them.
Visual search
Ask a design question, see the answer
Ask across every homepage, pricing and AI feature teardown, and the answer arrives with real UX components placed right next to the point they prove, drawn from a library of 4,000+ taken from real product pages.
4,000+UX components from homepage, pricing and AI feature teardowns
Visual search
Searches homepage, pricing and AI feature teardowns
Deep Research
Get weeks of product research done in minutes
Ask a broader question and DoP Deep Research searches the whole library, then writes it up. All of these sample reports were generated by the platform:
product signals read and judged, so you only see what actually matters
sources monitored continuously — blogs, changelogs, filings, job boards
product leaders tracked — who they are and what they say
homepage, pricing and AI feature teardowns
Why this is different
This is not competitive intelligence. It is product intelligence.
Knowing exactly what the world’s best product teams are building is genuinely hard to find out. It is scattered across changelogs, engineering blogs, pricing pages, job boards and earnings calls, and nobody assembles it — so most product leaders end up working from a conference talk and a screenshot someone dropped in Slack.
Department of Product does that one job: understanding how leading product organisations actually operate, and turning it into your own strategy and ways of working.
Ask it the questions you can’t just look up:
- How are companies using AI agents internally?
- What new voice UX opportunities should I be aware of?
- What are product leaders saying about the future of design?
- How are companies pricing AI features?
- What new homepage patterns are being shipped?
- How could my company use the new technologies that are emerging?
From the newsletter
Practical AI knowledge for people building products
The new UX of AI Agents
Memory you can edit, approvals people read, and agents that work while you’re away. 48 real world examples from 26 companies.
Oct 6, 2026
OpenAI Dots and the rise of Ambient Agents
Plus: the “one true SaaS layout”, Google Stitch gets a CLI, Replit interactive charts and more.
Oct 4, 2026
How Stripe built an internal prototyping tool that’s created 12,000 prototypes so far
And how you can build your own. Plus, other examples of new design tools and technologies.
Sep 28, 2026
Meta’s Muse comes to your keyring, but Amazon isn’t happy
Plus: Figma’s CEO says UIs won’t disappear, YouTube and Spotify ship algorithmic controls, and more.
Sep 27, 2026
How Figma Built a Product OS that saved PMs 2 hours a day
And how you can build your own version. Plus 15+ examples of internal AI tools from Pinterest, Airbnb and more.
Jul 13, 2026
Cursor’s new team mate and Gemini’s 1 billion milestone
Plus: YouTube’s CEO unveils his AI strategy, and how Ramp built product integrations that write themselves.
Aug 16, 2026
AI Agents in Practice
50+ real world examples of how companies are using AI agents internally. Square, DoorDash, Cursor, Replit, Gusto and Figma.
Sep 1, 2026
How to build a Self-improving Product
Is this the future of product development? Real world examples with templates and guides to follow.
Jun 22, 2026See more of the platform
Learn from the best product teams in the world.
See the platform with your own company set up inside it: the product teams you want to learn from, your homepage and pricing scored against the same rubric, and your AI maturity placed on the same scale as the teams you admire.