For product managers who ship with agents
Supaprod
agents that know what to build,
ship it, and guide the next call.
ship it, and guide the next call.
Pre-seed briefing·August 2026·Confidential
Three minutes · press →
01
01What we are building
One system, three layers.
01 · The director
It tells you what to build.
Reads your feedback, data, competitors, and market. Tells you what is worth building next, evidence attached. Your product taste becomes a system.
02 · The loop
It hands the work out, and checks what came back.
Discover to learn, one governed loop. Agents do the work in your own stack. You make the calls.
03 · The shared brain
It learns, and it guides.
Every decision recorded with its evidence, then graded against what happened. It compounds: next time it tells you what is right, and warns you before you repeat what was wrong.
the complete lifecycle · run by agents · gated by you
it tells you what to build. builds it. ships it. checks the outcome. learns. the more it runs, the sharper it gets.
02
02The problem
Every craft got its AI-native home.Product decisions still live in Slack.
Code
Claude Code · Cursor · Codex · v0 · Lovable
agent-run
Design
Figma · Framer
agent-run
Docs
Notion AI · Gemini
agent-run
Work
Linear · Jira
system of record
Decisions
heads, threads, scrollback
no home
“So why did we decide on X? Cue hours of finding that Slack conversation from months ago.”
Top-voted thread · r/ProductManagement · 480 points
Nobody knows what to build, so teams build on gut: 80% of shipped features are rarely or never used (Pendo). The one record a team needs most is the one nobody keeps.
03
03Why now
AI made building cheap.Deciding what to build is the bottleneck.
What just happened one layer below us
$2B+
Cursor annual revenue, four years after launch
CNBC · Feb 2026
75%
of new code at Google is now written by AI
Sundar Pichai · Apr 2026
8 mo
for Lovable to reach $100M ARR. Now past $500M
TechCrunch · Jun 2026
Every engineer got an AI pair. The person deciding what they build got a chatbot.
Cheap building multiplies the cost of bad deciding, and the agent budget is moving up a layer. That layer is unowned. PMs at OpenAI and DoorDash already hand-build private versions; one spent 1,500 hours. Nobody does that for a mild annoyance.
04
04The insight
Judgment has no compiler.
Code has a fast oracle: tests pass or they fail. That tight loop is why AI coding exploded and commoditized. A product decision waits months for its verdict. Judgment never got its loop.
signal→
decision→
evidence→
shipped→
outcome→
was the reasoning right?
The track record is the compiler for judgment.
05
05The loop, live · one run end to end
Twelve steps. Nineteen minutes.You made two calls.
A morning on Supaprod: it spots the problem, proposes the fix, you approve, agents ship, and D+14 grades the call.
08:02
it notices
your two largest accounts hit the same wall this week, and renewal season starts in sixthe director
09:14
it proposes
fix the export flow first: here is the revenue at stake, and the two cheaper options it rejectedthe director
09:14
it learns
the last time a big account hit friction and you waited, they churned. Here is the evidencethe brain
09:16
you approve
one click. The only human moment in the run
09:17
agents ship
design, code, tests. Live behind a safety flag the same daythe loop
09:17
if it fails
any failed check stops the run and returns it to your gate. Nothing ships without youthe loop
D+14
it grades
exports up, tickets gone, both accounts renewed. Verdict saved, so the next call starts sharperthe brain
live run · agent mesh3/5 working
Scoutclustered 3 signalsdone
Architectspec locked, 4 criteriadone
Designerscreens drafted from specrunning
Buildercode written, checks greenqueued
Sentrywatching D+14 outcomequeued
Agents ran twelve steps in nineteen minutes. You made two calls. Every one is on the record.
A full run costs cents, not sprintsReplayable, step by step, in the app● director proposes ● OS ships ● brain learns
06
06The moat · the shared brain
It learns your taste,not just your tasks.
Verdicts take weeks, so the record accrues in calendar time. What nobody can reconstruct afterwards is what you believed before the outcome landed. A forecast leaves no trace unless something wrote it down at the moment you decided. A competitor starting next year starts at zero, next year.
Quarter two starts smarter than quarter one.
Three pillars · own the loop · sense continuously · keep the evidence
07
07The market
Two budgets are convergingon one layer.
The tools budget · PM software
$3.8B today
Steady, unglamorous, growing 14% a year, and about to be re-platformed by agents.
MarkWide Research · May 2026
The agent budget · ungoverned
5% → 40%
Share of enterprise apps shipping task-specific AI agents, 2025 to end of 2026. Every one creates decisions nobody scores, and unscored decisions never improve.
Gartner · Aug 2025
2.6M people hold the product manager title on LinkedIn. The wedge customer: the founding PM already shipping with agents. Land at $20 a month, expand to the team, and grow on decision volume, not headcount.
Launch · Sep 2026
Public and self-serve from day one. Every consequential call human-gated.
Scale · immediately
No slow-burn roadmap. Individual PMs to teams to orgs, meter on, aggressive from the first week.
Enterprise · fast follow
Governance and audit. The track record becomes the org's institutional memory.
Autonomy expands per lane, earned on track record. Strategic calls stay human, permanently.
08
08The opportunity, sized
The tools budget is billions.The work budget is hundreds of billions.
TAM$300B+ / yrthe PM work budget
What 2.6M PMs are paid to do: know what to build, ship it, learn from it. Agent swarms move that spend from headcount to systems.
SAM$2B → $12B / yrlaunch pricing → value pricing
MOTION 1 · TRANSFORM 650K existing product teams MOTION 2 · CREATE 500K new agent-native orgs by 2030
SOM~$47M ARRthe entry wedge · today
The agent-native tenth of existing teams, at launch pricing. One layer below, Cursor crossed $2B ARR by year four on this same curve.
The spend follows the work, not the seats · full arithmetic in appendix B
09
09Business model
Priced on outcomes.Agents do not have seats.
A credit pays for a finished result: a closed decision loop. Never tokens, never chat, never seats. A teardown runs 15 to 40 credits; idea to shipped, 150 to 400. The price of an afternoon of coordination.
1 · Land
Free teardown of your riskiest idea. No card.
2 · Convert
Pro $20/mo solo · Business $50/mo team, credits included.
3 · Expand
Heavy shipping months buy top-ups. Spend grows toward a tenth of one PM's cost per team, tracking decisions closed, not headcount.
4 · Enterprise
SSO, compliance, audit, BYOK. Governance is what enterprises pay for; the audit trail is what they are buying.
Credits pool at the account level, never per seat, so shared memory is never taxed
Seat pricing shrinks as PM work automates. Credit pricing grows with every decision the org makes.
10
10The field
Everyone owns a step.Nobody owns the loop.
The vertical axis compounds: every graded outcome moves us up. That is the moat
None of them records whether the call was right. That needs the whole loop under one roof, and the belief you held going in cannot be recovered after the fact.
Our real competitor is the stitched stack: five tools and the PM as the glue.
Not a wrapper on a frontier model. The models are interchangeable parts; the system is ours: the loop, the gates, the track record. Our engine runs the best model for each job, in your repo.
A frontier lab ships capability. The accountability layer across your tools is what it will not own.
Field and data detail in appendix
11
11Where this stands
Built. Running. Opening.
Listened first: pain research across PM communities, practitioners, and the stitched-stack workflow
Done
Prototyped, built, rebuilt. Kept what survived contact with real use
Done
The full loop, discover to learn
Running today
Self-serve signup
Live
Fine-tuning the experience, folding in early feedback
In progress
Early access invites, from a named list
Next
Public launch
September 2026
Pre-revenue, pre-launch, said plainly. The working engine is the proof; the cohort is the next one.
12
12Team
Built by user zero.
Experience
Intellect
Senior AI product manager at Intellect, a leading BFSI technology OEM serving 200+ financial institutions across 70+ countries. Bangalore, India
Infineon
Product manager at a leading global semiconductor company. Munich, Germany
ISRO
Associate product manager, communication systems at India's national space agency. Systems that flew, built at 21, where a mistake is unrecoverable. Bangalore, India
Education
TUM
MBA, TUM School of Management, one of Europe's top business schools, at the Technical University of Munich. Munich, Germany
Why this founder
Ten years being the glue this product replaces: carrying context across a dozen tools and answering “why did we decide this” from memory, months later. Shipped where a wrong call is unrecoverable: space, silicon, banking. He built the thing he needed, and runs on it every day.Built for agents to run
Supaprod is built the way it expects product teams to work: one human on judgment, an agent swarm on execution, every step gated. Solo, in weeks. The proof of the method is the product itself.Agents do the work.
You answer for it.
You answer for it.
Supaprod is how you answer.
● tells you what to build·● runs the work·● learns, and guides the next call
Every product org is about to run on agents. The winners will know what to build, ship it without drag, and get sharper with every call. That is not a feature on a tracker. It is a different way for product to run. We are building it.
Sign up for the beta → supaprod.ai
A
A1Appendix · the field in detail
Who is who, and what they do not keep.
| Relation | Who | What they do | What they do not |
|---|---|---|---|
| Race | Productboard Spark (agentic relaunch Jun 2026) · Atlassian Product Collection (May 2026) · Samepage Signals ($4.85M seed, Jun 2026) · Brief · Notion AI · Linear's AI agenda | AI on top of feedback, roadmaps, tickets, and docs. Sense and summarize; some draft and prioritize. Each owns a part of what we do. | None ships the work or grades the outcome. The loop never closes, so nothing learns. No decision-and-outcome record. |
| Absorb | ChatPRD (50K+ users) and AI spec writers | Excellent single artifacts: PRDs, specs. Proof that PMs pay for AI help. | A spec is one step of one stage. No loop, no record, no verdicts. |
| Engines | Frontier models via API (Claude, GPT, Gemini) · era tools: Claude Code, Cursor, Lovable, Devin, Windsurf, JetBrains agents | Raw building capability. Our own build engine runs the same models inside the governed loop, in your repo. | Capability is not a system. No gates, no track record, no accountability for what was built or why. |
| Sense silos | Dovetail · Enterpret · live channels: Intercom · Zendesk · Slack | Feedback and research repositories, and the channels customers actually speak in. Strong at remembering what users said. | Insight stops at the document. Nothing ships, nothing is graded, nothing learns. |
| Rails | GitHub · Linear · Jira · Notion · Figma · CRM: Salesforce · HubSpot | Systems of record for code, tasks, docs, design, and who the customer is. We read and write them. | No home for decisions, evidence, or outcomes. The why still dies in threads and pages. |
| Measure | Amplitude · Mixpanel · PostHog · Statsig · LaunchDarkly | Where the result of a shipped bet actually lands. We read them; this is the raw material of a verdict. | Hold the number, never the decision that caused it. Nobody joins the outcome back to the call, so nothing re-ranks the next bet. This is the gap we are. |
We touch six surfaces and compete on one: whether a shipped bet's outcome is verified and fed back into the next decision. Everywhere else we read, absorb, or run on top of. Full competitive Q&A brief available on request.
B
A2Appendix · the numbers
Every figure in this deck, sourced.
| $1B → $2B+ | Cursor ARR, Nov 2025 to Feb 2026; $29.3B valuation | CNBC |
| $100M / 8 mo | Lovable ARR ramp; $500M+ by Jun 2026 | TechCrunch |
| 75% | of new Google code AI-generated, up from 50% six months earlier | The Verge · Apr 2026 |
| 20M · 90% | GitHub Copilot all-time users; share of Fortune 100 using it | Microsoft FY25 Q4 |
| 80% | of shipped features rarely or never used, across 615 products | Pendo Feature Adoption Report |
| 5% → 40% | enterprise apps with task-specific AI agents, 2025 to end of 2026 | Gartner · Aug 2025 |
| $3.8B → $12.6B | PM software market, 2026 to 2035, 14.2% CAGR | MarkWide Research · May 2026 |
| 2.6M+ | people holding the product manager title on LinkedIn | Productify · May 2026 |
| $300B+ / yr | TAM math: 2.6M PMs (LinkedIn) × ~$115K average fully loaded cost (salary, benefits, overhead, blended globally) = $299B of product-management work paid as headcount today | Internal · transparent math |
| $2B → $12B | SAM math: 650K existing teams (2.6M PMs ÷ 4) + 500K new agent-native orgs by 2030 = 1.15M orgs. At launch pricing ($150/mo): ~$2B a year. As autonomy earns value pricing (~$10K a year, a tenth of one PM's cost): ~$12B a year, about 4% of TAM | Internal · transparent math |
| ~$47M ARR | SOM math: 10% of existing teams are agent-native today (Gartner trajectory: 5% to 40%) = 65K teams × ~$60 a month blended launch pricing = $47M a year, before value pricing | Internal · labeled scenario |
Prepared August 2026.