Confidential
Version control for agents.
The new reality

Agents write a growing share of our software.

Share of new code written by agents
2023
~10%
Today
~40%
Ahead
~90%
Software is increasingly written by agents, and the shift is only accelerating.
The new reality

Agents now work in parallel and make trying ideas cheap.

agents
Editing
implementing JWT refresh feat/auth nas claude-01
In review
indexing search results feat/search nas sarah codex-02
Done
rate-limiter token bucket feat/ratelimit nas alex cursor-03
icon component refresh fix/icons alex claude-05
test fixtures cleanup chore/tests nas sarah codex-06
oauth provider docs docs/oauth sarah gemini-07
Idle
JWT spike research spike/jwt nas gemini-04
Every agent runs in its own session and branch, shared with the right people. Trying an idea is now cheap.
01   Problem

Git was built for one developer, a few commits a day, alone on a branch.

It’s 2026, and it is still a pain to:
× Parallel work
Run parallel agents without them trampling each other’s work.
× Local context
Share and collaborate on local agent files and context.
× Trust
Trust agents not to delete or break files as they go.
× Tool sprawl
Copy-paste between Notion, Linear, and GitHub just to collaborate.
02   Problem

Git versions a sliver. Everything else agents touch is invisible.

Versioned today
Code
Not versioned at all
Environment Plans Decisions Context Agent work Reviews & comments
Git was built for one human, working sequentially. So teams throttle their agents instead of unleashing them. The bottleneck is no longer the model, it’s the infrastructure to run, review, and trust what agents produce.
03   Current solutions

Today, teams duct-tape four tools. It still breaks.

× Git + GitHub: versions code and nothing else. Built for one human committing in sequence.
× Notion: where plans and specs live. Not versioned, not connected to the code they describe.
× Linear: where work gets tracked. Disconnected from the actual files agents are changing.
× Agent IDEs (Cursor, Devin): run agents, but no shared workspace, no team review, no memory across runs.
Four disconnected tools, none built for agents. The work agents actually do falls through the gaps between them.
04   Vision

Self-improving agents.

The next leap isn’t a smarter model. It’s agents that upgrade themselves. To do that safely, an agent has to branch off, change its own code and environment, and test the upgrade in production before keeping it.
Branch the live system upgrade itself test in production keep what works
None of this is possible on today’s tools. Self-improving agents need far more powerful version control: branch, run, and merge the whole system, not just code. That’s the infrastructure Mod is building.
05   Solution

Mod is version control for the whole system.

Connect a local agent session auto-created tracked in parallel structured context
06   Demo

Your collaborative agent harness.

Sessions Canvases Connect your agents Checkpoints & revert Extensible harness
07   Market

The market expands as software starts being grown.

$1T+
$300B
$80B
$80B · Now
Agent infrastructure for engineering teams. The workspace teams build with agents in.
$300B · Next
All software development. As agents do the work, every team’s tooling spend follows them.
$1T+ · Expansion
Every business runs its own software. When software is grown, not shipped, the buyer is no longer just engineering.
Agent compute is already a line item and spend per team is compounding fast. The next buyer isn’t a developer at all.
08   Competition

Productivity apps bolt AI on.
Mod is the infrastructure underneath.

The market we take Notion Linear Slack GitHub Google Drive
Productivity & collaboration apps, built for the human era.
What the agentic age needs
Productivity apps
Mod
Versions the whole system, not a silo
Agents as first-class collaborators
Parallel agent work: branch & merge
Checkpoint, attribute & roll back
Local-first, runs on your infrastructure
One programmable substrate, not a fixed app
An app can add AI features. It can’t become infrastructure. That gap is the whole opportunity.
09   Team

Built by the people who built the primitives.

Nassar Hayat
Nassar Hayat
Co-founder & CEO
Head of Platform at MultiOn, building browser agents. Led the developer ecosystem team at Radicle, a peer-to-peer GitHub alternative. Co-founded Foodchain, a B2B platform bootstrapped to $5M ARR.
Alex Good
Alex Good
Co-founder & CTO
Lead maintainer of Automerge, the leading open-source CRDT library, at Ink & Switch. Led the protocol team at Radicle. Co-founded Foodchain. Speaker at Local-First Conf and QCon London.
Years of R&D on local-first collaboration and agent architectures. We have shipped together before. Longtime collaborators, and each other’s best man.
10   Business model

We sell the infrastructure
agents run on.

$0 $100M $200M $300M 0 25k 50k 75k ↑ GitHub · 4M+ orgs (off chart) Cursor · ~50k teams Launch Year 1 Year 2 Year 3
Revenue (ARR) Teams
We launch in ~3 months. Year-3 target: Cursor’s ~50k team scale and $250M ARR (~$5k per team).
Free Get started
Collaborative workspace
Real-time sync
Backup storage
Bring your own model keys
Pro For teams
Everything in Free
Bundled inference
Storage and hosted VMs
3× model usage
Max Agents at scale
Everything in Pro
20× inference
Dedicated VMs
Priority sync
Every plan bundles OS inference Storage Hosted VMs Sync
11   Traction

Live today. Technical leaders are already pulling for it.

“Give me a way to share my agent data with the team and I will stop using Linear, GitHub, and Notion.”
Engineering leader
“Our product and engineering teams are stuck in so much grunt work trying to organise and collaborate on plans.”
Engineering leader
“We’re struggling to manage agents across Notion for specs, Linear to track, and a separate GitHub repo for local context.”
Engineering leader
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Name, role
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Shipped and in production: the CLI (npm install -g @mod/mod-cli) and the real-time collaborative workspace.
12   The ask

Raising to reach the first 1,000 teams.

$X.XM
~18 months of runway to grow the team below and put Mod in front of its first 1,000 teams.
Use of funds
Product Engineering2 hires
Infrastructure Engineering2 hires
ML Engineering2 hires
Infrastructure & inferencecompute
Sales & marketingGTM
mod.computer nas@mod.computer
Mod
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