Product Intelligence Platform

Turn product intelligence into product strategy

See what the world’s leading product teams launched, priced, designed and built for themselves, how they organise, and what happened next. Then research, test and decide your next move with 20,000+ judged signals behind every call on your roadmap.

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The platform

20,000+ product signals, read and judged so you only see what matters

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Product Intelligence

Signals

Track launches, feature releases, internal tooling, hiring, and positioning shifts in one place.

20,000+ signals judged · 6,500 active

Showing 1–6 of 3,317

Explore product intelligence

Tracking product excellence at world-leading teams

What you can do with it

Decide what to build next, grounded in real market evidence

Signals20,000+

AllLaunchFeatureInternal toolAI adoption

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

Signals · Internal toolFiltered

AllLaunchFeatureInternal toolAI adoption

VercelAI factory now authors 35% of merged PRs, closes 70% of issues in AI SDKInternal tool

ShopifyCompresses production failures into model weights daily, cutting serving costs 96%Internal tool

DoorDashBuilt Flux, a cloud platform for agents with sandboxed execution and audit trailsInternal tool

NetflixBuilt a real-time distributed graph query layer serving billions of nodes at sub-100msInternal tool

LinkedInRebuilt search infrastructure on an LLM-based stack for scaleInternal tool

Product Intelligence20,000+ product signals

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.

PerspectivesThe people shaping how product gets built

Tap into the world’s leading product thinkers, in one place

The founders, CPOs and heads of product whose thinking you would otherwise chase across a dozen podcasts, newsletters and conference stages. When one of them publishes something worth reading, it arrives in your feed as a Perspective signal with their name, role and company against it.

500+ product leaders followed in one place, so their thinking reaches you without the hunt.

Internal toolsWhat product teams build for themselves

Discover what product teams are building internally

Every team is quietly rebuilding how it works: an agent that writes a third of the code, a sandbox where agents can run safely, a pipeline that made inference affordable. These are the decisions that change what a team can ship next year, and they are the hardest to find out about — nobody puts internal tooling on a roadmap. See what new internal tools leading teams have actually adopted and use it as inspiration to transform your product operations.

A view of how the best product teams are really working, not the version they present at conferences.

AssistantThe questions behind a strategy

Ask the product questions that shape your strategy

The questions that decide a quarter are rarely ones you can look up. Should AI sit in the base tier or behind an upgrade? Is anyone actually making usage pricing work? How are teams measuring whether an AI feature landed? Ask them here and the answer comes back from what the market has already done — with the signals behind it attached, so you can take the evidence into the room rather than the opinion.

A defensible position on the decision in front of you, in the time it takes to ask the question.

Strategy Workspace

Make strategic decisions grounded in your market’s signals

Put the question in the middle of a Thought Canvas and build the case around it, with evidence from Product Intelligence, the realistic options and a framework run over them. Or ask the Thought Partner and get an answer drawn from what the market has already done. Either way, every claim shows where it came from, and the finished work becomes a deck or a document to share.

Strategy Workspace / Thought CanvasSavedYour canvasesNew canvasMake the decision →

Evidence

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?

Options

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.

Frameworks

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.

Choose it myselfAdd evidence

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.

Should we move from per-seat to usage pricing?Are we losing on price or on positioning?Where is there uncontested space for us?What could we stop competing on entirely?Are we differentiated, or just different?What would we build if we started today?Which of our assumptions is most out of date?

Build our own AI layer, or partner?Should we go upmarket or deepen the core?Where could we change the rules of our category?What makes us genuinely hard to copy?What would a well-funded new entrant do to us?Which emerging behaviour should we bet on?

What should we stop doing next quarter?Do we charge for AI, or bundle it in?What is everyone in our category getting wrong?What would make a customer switch away from us?What should we build that nobody else is?Where is the market heading that we are not?

Explore the Strategy Workspace

A growing library

500+ teardowns and 5,000+ UX components — homepages, pricing, AI features and more

AI Features

CursorDesign mode

Point at the page, and the agent gets the instruction

Clicks, CSS edits and freehand drawing on a live webpage become numbered instructions a coding agent can read and act on.

Inline property panelNumbered annotation badgesFreehand draw-to-promptDiff-before-send

AI Features

AnthropicClaude Duet

A colleague’s Slack message starts the investigation

One message becomes a self-driven investigation that pulls data from Amplitude, compares flows in Figma and drafts a Canva deck, all in one chat thread.

Notification-as-triggerInline tool widgetsNamed connector rows

Pricing

StripeScores 85 / 100

Transparent per-transaction pricing, no sales call

Price transparency5

Tier clarity4

Comparison density4

AI packaging clarity4

Transparent default pricingInteractive calculatorProgressive complexity

AI Features

NotionAgent tasks

Agents that run for 24 hours, then check their own work

Agents get their own compute and browser for up to 24 hours, an Auto model picker for each task, and a Review for me mode.

Collapsible status lineInline stop controlTiered tool menu

AI Features

FigmaAI Design Agent

An agent that works on the canvas, not in a sidebar

Capability framing4

Trust & control4

Discoverability3

Output editability5

Native-canvas embedding, not a sidebarDesign-system-aware generation

NetflixNatural Language Search

Search by mood, act on the result immediately

Query expressiveness5

Result relevance4

Path to action5

Mode legibility3

Mood-phrase query over title/genre matchImmediate action from results, no detour to a detail page

AI Features

AirtableOmni 2.0 and Canvas

Drop in spreadsheets and decks, get a base and a dashboard

Omni 2.0 turns dropped files into structured bases, then Canvas turns that data into a generated dashboard page.

Multi-file drag-inPrompt-to-dashboard generationMetric card row

Homepage

PostHogProduct-led

Shift your product into self-driving mode

Proposition clarity4

Visual hierarchy3

Distinctiveness5

Information density4

Left sidebar navigation menuProduct demonstration showcaseSocial proof statement

Homepage

DeelConversion-led

Hire, manage, pay, & equip anyone, anywhere.

Proposition clarity5

Visual hierarchy4

Distinctiveness3

Information density4

Hero section with dual layoutDual review badgesLogo wall social proof

Homepage

LinearProduct-led

The product development system for teams and agents

Proposition clarity4

Visual hierarchy3

Distinctiveness4

Information density4

Live product UI in the heroNamed agents beside human teammatesDecimal section numbering

Explore UX 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:

See how Deep Research works

20,000+

product signals read and judged, so you only see what actually matters

400+

sources monitored continuously — blogs, changelogs, filings, job boards

500+

product leaders tracked — who they are and what they say

500+

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?

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Deep Dive

How Figma Built a Product OS that saved PMs 2 hours a day

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Briefing

Cursor’s new team mate and Gemini’s 1 billion milestone

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Deep Dive

AI Agents in Practice

50+ real world examples of how companies are using AI agents internally. Square, DoorDash, Cursor, Replit, Gusto and Figma.

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Knowledge Series

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Jun 22, 2026

Browse all articles →

See more of the platform

Product intelligenceThe evidence: what leading teams shipped, and what happened next.Strategy workspaceWork out what to build next, from first question to decision brief.UX teardownsSee how leading products designed it before you build yours.Org design and simulatorShape the team that will build it, and model the change first.

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.

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