Persistent systems,
not session tools.
The AI agent industry is bifurcating. One side builds tools — session-scoped, user-present, interactive. The other side builds systems that keep context, run recurring work, and compound. Tools reset when you close the tab. Systems do not.
yarnnn builds the second category: persistent agents, shared workspace context, TP orchestration, and recurring task execution for knowledge work that repeats every week whether the user is present or not.
We're raising $500K pre-seed at $5M post-money.
The raise
$500K
Pre-seed round
$5M
Post-money valuation
$9–19
Per month pricing
$1.14B
SAM
150+
Architecture Decision Records
Use of funds
Senior Tech Lead — accelerate execution pipeline and agent architecture
GTM Lead — drive adoption in solo consultant wedge
Candidates identified from enterprise consulting network
Stage
Delaware C-Corp, pre-revenue
MVP live with autonomous agent execution shipping
Solo founder — full stack built and shipped independently
The bifurcation
OpenClaw hit 307K GitHub stars in 60 days. Claude shipped Cowork. The demand for AI that does real work is proven. But every one of these products is a tool — session-scoped, user-present, stateless between runs. Recurring knowledge work requires something structurally different.
AI Tools
Session-scoped. User must be present. Context resets or degrades between uses. Quality of session 51 is roughly equal to session 1.
OpenClaw — local agent, dies when terminal closes
Claude Code — session-scoped, CLAUDE.md is static
Cowork — desktop agent, no autonomous execution
ChatGPT — memory is facts, not domain expertise
Persistent agent systems
Persistent. Autonomous. Run recurring tasks without the user. Feedback becomes learned behavior. Quality compounds with tenure. Day 90 output is irreplaceable.
yarnnn — workspace, agents, TP, tasks
Cloud-native by structural necessity, not preference
The local-first wave proves the demand. Every OpenClaw user who automates recurring tasks locally will eventually want those tasks to run without them, accumulate learning, and deliver on schedule. That graduation path — from tools to systems — is yarnnn's market.
Why persistent systems require cloud
This isn't a preference. It's the structural requirement of the problem space.
Runs without you
Agents execute at 6 AM Monday. Your laptop is in your bag. Cloud compute is the only option for autonomous scheduled work.
Accumulates over months
90 days of Slack patterns, feedback history, and domain knowledge requires persistent storage that outlives any session.
Cross-platform sync
Server-side OAuth and always-on polling. Can't sync Slack and Notion while you sleep.
Multi-agent coordination
Research Agent feeds Content Agent on shared state. Local filesystems are single-tenant.
Feedback compounds
Every edit teaches every future run. Persistent memory across weeks, not sessions.
What's live
Scaffolded workforce + YARNNN
Every user gets the current 9-agent scaffold at sign-up: five universal specialists, Reporting, three platform bots, and YARNNN. The point is not the list itself. The point is that the system starts with persistent workers and a meta-cognitive controller.
Autonomous task pipeline
Three-layer architecture: mechanical scheduling (zero LLM), multi-agent execution (Sonnet), conversational orchestration. Purpose-built, not an API wrapper.
Slack + Notion + GitHub
Always-on sync. Cross-platform context accumulates with every cycle. Bots activate when you connect a tool.
Shared context domains
Six structured domains (competitors, market, relationships, projects, content, signals) where agents deposit and refine intelligence across cycles.
Compounding moat
90 days of accumulated context, feedback, and domain knowledge can't be replicated by downloading a new tool. Switching costs increase automatically.
Investment thesis
The AI agent market is proving massive demand (OpenClaw: 307K stars in 60 days). But every viral product in this wave is a tool — session-scoped, stateless, resets between uses. The structural requirements of recurring knowledge work (persistence, scheduling, cross-platform sync, feedback loops) can only be met by cloud-native architecture. This creates a natural bifurcation: tools for interactive work, systems for autonomous work.
yarnnn is building the system layer: persistent agents, a shared workspace, TP orchestration, and recurring tasks that accumulate domain expertise and deliver compounding work products. The subscription model is straightforward: pay for a system that keeps running while you sleep.
The local-first wave isn't competition — it's demand validation. Every user who automates recurring work with a local tool will eventually need it to run without them. That graduation from tools to systems is yarnnn's market.
Market
$4.35B
TAM — AI productivity tools, 31% CAGR
$1.14B
SAM — 5M solo consultants at $228/yr
$11.4M
Entry SOM — 50K users in 3 years
Entry wedge: solo consultants managing multiple clients with recurring tasks across 3+ tools. Clearest pain, shortest sales cycle, highest willingness to pay. Expansion: founders, executives, teams, then every knowledge worker who wants to supervise recurring work instead of rebuilding it in chat tools.
Founder
Kevin Kim — Solo Founder & CEO
Korean-born, US-based. A decade of work spanning enterprise systems, cross-border operations, and context architecture — from deploying CRM for Japan Tobacco in post-military Myanmar to building GTM systems for cross-border sales teams.
Shipped the entire MVP solo: full-stack application (Next.js + FastAPI + Supabase), platform integrations, autonomous agent execution pipeline documented across 150+ Architecture Decision Records, and a working context accumulation engine — all before raising a dollar.
Let's talk.
If you're investing at pre-seed in AI infrastructure, autonomous agents, or the future of knowledge work — I'd love to share the deck and walk through the architecture.
kvkthecreator@gmail.com