Confidential · 2026

Build products at the
speed of thought.

02. The Change
The constraint has moved

Writing code is no longer the bottleneck.
Knowing what to build is.

The old world
Writing code = scarce, expensive, slow.
Everything organized around execution.
The new world
Writing code = commodity.
The constraint moves upstream to knowing what to build.
03. Stakes
The efficiency threshold

A threshold is approaching.
The window is closing.

Can your product be rebuilt faster than you can improve it? If yes, you're below the threshold.

Below the threshold
Commoditized overnight.
  • Defensibility built on features. Features get rebuilt in hours.
  • Slow iteration. Manual handoffs. Broken feedback loops.
  • Your product takes months to improve. Someone rebuilds it in a weekend.
  • No compounding. Every cycle starts from scratch.
Above the threshold
The ones that survive and thrive.
  • Defensibility built on iteration speed. How fast you learn what users need and ship it.
  • Closed loops between discovery, execution, and measurement.
  • Every cycle compounds: faster, smarter, more aligned with users.
  • Features can be copied. Your speed of learning can't.

When execution costs nothing, the only moat is your iteration speed. The teams that compound will survive. The rest will be rebuilt by someone faster.

04. The Evidence
Where does all the time go?

Teams are about to free up half of their time with AI-assisted execution.

Because execution was the limiting factor for so long, squads have been disproportionately built around engineering. Now that code is becoming a commodity, execution takes 50% of squad time but drives only 11% of product outcomes.

Squad time allocation vs step importance · 16-person team
Step importance Productive hours Coordination overhead

Based on modeled squad: 1 PM · 2 designers · 8 SWEs · 1 DS · 2 QA · 1 DevOps · 1 Analyst

4.6×
Execute overallocated vs its importance
56%
Of outcomes determined before code is written
23%
Of squad time spent on those steps
Read the full methodology and research report →
05. The New Paradigm
Only one keeps the human in control without slowing everything down

Three ways to build with AI.

Automated Squads
AI-enabled people in silos.
PM
+ AI
→ ← →
Design
+ AI
→ ← →
SWE
+ AI
→ ← →
QA
+ AI
Constant back-and-forth. Context lost at every handoff.

Each role is faster with AI. But the human is still the interface between every step. Information degrades at every handoff.

AI doesn't learn

Each AI tool is stateless. No shared context. No memory of what the human wants. Every cycle starts from zero.

Semi-Autonomous
AI moves fast. Human decides. AI learns.
PM
Design
SWE
QA
↕
↕
↕
↕
ONE AI · FULL CONTEXT · ALL ACTIVITIES
Asks the right person the right question at the right time
↻ Learns from every interaction

AI has full context across all steps. Moves at the speed of thought until it needs the human. The human steers. AI executes.

AI learns from every interaction

Every human decision teaches the AI what the builder wants, how they like to work, and the UX they're trying to create. Each cycle is better than the last. This is Tempo.

Fully Autonomous
Human at the edges only.
You
prompt
→
?
no visibility
→
You
QA · 70%
✗ No steering. No memory. No depth.

Fast, but a black box. Human provides instructions, AI runs unsupervised, comes back with a 70% solution. No way to steer mid-process.

Shallow learning only

AI iterates on outputs when told, but the human can only react to results, not steer the process. No understanding of the builder's intent or quality bar.

06. The Promised Land
Welcome to the age of the product builder

Hybrid teams, humans and AI agents, where humans set the direction and AI closes every loop.

1
AI channels context between every step
No more copying from meetings into docs, docs into tickets, tickets into code. One system holds the full context.
2
You set the destination. AI takes the turns.
Like an autonomous vehicle: you decide where to go. AI navigates. When it hits something ambiguous, it asks you.
3
Discovery to measurement in one system
Every step feeds the next. What users said informs what gets built. The process compounds each cycle.
07. The Obstacles
But three barriers stand in the way

The promised land is real.
Getting there is not obvious.

01
The Handoff
The handoff between "what should we build" and "what gets built" is where most teams lose. Connected means constant back-and-forth. Disconnected means drift.
02
Scaling
You cut corners to validate quickly, but there's no system to track what was cut and circle back. The prototype becomes the product by accident.
03
The Loop
Features ship but users who requested them never know. Corners cut to validate never get revisited. Every cycle starts from scratch.
08. The Product
What if the entire path from user need to shipped feature lived in one place?

That's why we built Tempo.

Eliminates the handoff
Capture

Tempo joins your meetings, captures every user need, and structures it into specs that flow directly into execution. No drift between what users said and what gets built.

Scales hypothesis to production
Build

AI agents execute with structured review gates. The product builder approves key decisions without reading code. Every shortcut is tracked and surfaced for revisiting.

Closes every loop
Learn

Users who requested features get notified when they ship. Corners that were cut get surfaced. Every cycle feeds the next. The process compounds.

09. The Map
Three stages of product automation

The market is moving through these stages right now. The window to own each one closes fast.

Now
AI generates,
humans fix

Lovable, Bolt, Cursor. Gets you to 70%.

18 months
AI executes,
humans direct

Structured review gates. Production-grade output. Human in the driving seat.

Tempo enters here.
3–5 years
AI builds from specs autonomously

Proprietary models trained on product intent. The spec is the product.

The teams that enter Stage 2 now will own the data to build Stage 3. The teams that stay in Stage 1 will be buying that capability from someone else, or they won't exist.

10. The Moat
Two compounding advantages

Every user interaction makes Tempo harder to replicate.

Moat 1. Compounding Context

The more a user builds with Tempo, the deeper we understand their product, their users, and how they like to work. This context compounds over time and becomes irreplaceable. Switching costs grow with every cycle.

The Flywheel
User builds
Spec → code → review
→
Context deepens
Preferences, intent, style
→
AI gets smarter
Better proposals
→
Harder to leave
Irreplaceable context

Only Tempo has the full loop: intent → code → human judgment → learning.

Moat 2. Proprietary Model: Spec-Grounded Verification Loop
Generator

Fine-tuned open-source model takes product spec + codebase context and produces candidate implementations.

Verifier

Separate model scores each implementation against the original spec: correctness, security, architectural coherence.

RLHF Loop

Every user approval or rejection is a training signal. The model learns what "good" means for product builders specifically.

11. Positioning
Lovable created a graduating class with nowhere to go

Tempo is where they graduate to.

Lovable / Bolt
User-friendly.
Fast prototypes.
Hits wall at 70%.
Can't ship.
Tempo
User-friendly.
Ships to production.
No code shown.
Cursor / Claude Code
Powerful.
Production-grade.
Requires engineers.
Not user-friendly.
The only product that's both user-friendly and production-grade.
12. Market
A massive gap between two massive markets

$36B in combined valuation. The middle is wide open.

Lovable
$7B valuation
$400M ARR
15M daily active users
New market. Explosive growth. 70% ceiling.
Tempo
the convergence
Cursor
valuation $29B
ARR $2B+
users (1M paying) 2M
Existing market. Engineers writing code. Proven demand.
Product builders need more structure and human oversight to get past 70% and actually ship.
As LLMs improve, engineers migrate toward directing AI rather than writing code.
Lovable bets AI can do everything. Cursor bets engineers will always be needed. We bet the answer is in the middle.
13. The Founder

Every company I've built led to this one.

AN
Antoine Neidecker, Founder
ML Engineer (3 years) · Head of Product (2 years) · 3× founder
mtg.ai
Meeting summarization startup. Literally the discovery feature reborn in Tempo
Train Fitness
Wearable startup, still growing. Proved ability to ship hardware + software product
Merkle (Head of Product)
Transaction order flow, still growing. Bridged product thinking and technical execution
Why this founder for this company
  • ML engineer building an AI product. Can design and build the verification loop and fine-tuned model in-house
  • Head of Product who lived the execution gap. Understands why product managers need this tool, not just engineers
  • mtg.ai founder. Already built the meeting-to-insight pipeline once. The discovery feature isn't new territory; it's a returning domain
  • 3× founder with two companies still growing. Pattern of building things that last
Product status
  • Discovery: live. meeting bot + transcription + AI analysis + feature prioritization
  • Execution: live. parallel AI agents, structured task lifecycle, review gates, verification
  • Next: connecting discovery to execution end-to-end, then building the proprietary model
Currently building design partner cohort of 10–20 product builders. Demo-ready today.
14. The Ask
The round

$8M seed to own the workflow before the window closes.

The barrier to building software is collapsing. The teams that close the loop first will compound. We're building the infrastructure that gets them there.

Use of funds
  • Fine-tuning infrastructure, RLHF pipeline, verification model
  • Design partner expansion, product builder community, GTM
  • Grow to 10 people: ML engineers, product, growth
18-month milestones → Series A ready
  • $2M+ ARR from paying product builders
  • First production feature shipped within 14 days of onboarding (average)
  • 90-day retention above 80%
  • Proprietary model live, outperforming base models on spec-fidelity
Where we are now
  • Working product. Discovery and execution both live
  • Building design partner cohort of 10–20
  • Model architecture designed: generator + verifier + RLHF loop
  • Ready to demo. Let's find a time.

Every product team will be reorganized around AI in the next two years.
Tempo is how they get there.

antoine@tempo.diy · tempo.diy