Building an automation platform that works when cloud-only tools fail—for businesses with unreliable internet.
THE CHALLENGE
Small-to-medium businesses—clinics, retail stores, agricultural cooperatives—need workflow automation but face a fundamental barrier: unreliable internet connectivity. Cloud-only platforms like Zapier and Make.com fail them precisely when automation matters most.
VALIDATED PAIN POINTS
— SMB Community Forum
— Clinic Administrator
— Agricultural Cooperative
COMPETITIVE ANALYSIS
| Requirement | Zapier | Make.com | n8n | Taktak |
|---|---|---|---|---|
| Works Offline | No | No | No | Yes |
| Zero-Setup AI | No | No | No | Yes |
| No API Keys | No | No | No | Yes |
| Desktop App | No | No | No | Yes |
DISCOVERY & VALIDATION
Monitored SMB forums, Facebook business groups, and Reddit communities for automation pain points. Found consistent themes around connectivity issues, cost sensitivity, and API complexity.
Evaluated Zapier, Make.com, n8n, and Retool. Mapped feature gaps and identified offline operation as a genuine whitespace opportunity.
Watched non-technical users attempt to set up Zapier workflows. Identified API key configuration as the primary abandonment point.
| Finding | Product Implication |
|---|---|
| 73% of SMBs in rural areas report weekly connectivity issues | Offline-first is a must-have, not nice-to-have |
| API key setup has 60%+ abandonment rate for non-technical users | Zero-setup AI option required |
| Workflow changes create anxiety ("what if I break it?") | Versioning with one-click rollback needed |
| Template adoption is 4x higher than blank-canvas starts | Pre-built templates are critical for onboarding |
| Privacy concerns block cloud adoption for healthcare/legal | Local data storage is a feature, not a limitation |
PRIORITIZATION & SCOPE
| Feature | Rationale |
|---|---|
| Offline-first architecture | Core differentiator; addresses primary pain point |
| Visual workflow builder | Non-technical users can't work with code |
| 4-tier AI fallback | Guarantees 99.9% uptime claim |
| Zero-setup local AI | Removes API key barrier entirely |
| Pre-built templates | Reduces time-to-value to minutes |
| Desktop app | Privacy positioning + revenue stream |
SMB users are often solo operators; enterprise features can wait
37 well-built nodes beats 400 broken ones; quality over quantity
Desktop covers primary use case; mobile adds complexity
Solo users first; collaboration is a scale problem
| Trade-off | Choice | Rationale |
|---|---|---|
| PouchDB vs PostgreSQL | PouchDB (local-first) | Enables offline operation without server infrastructure; aligns with core positioning |
| 4-tier AI vs single provider | 4-tier fallback | Complexity cost worth it for 99.9% uptime claim; eliminates vendor lock-in |
| Electron desktop vs web-only | Both, desktop as paid option | Web for discovery, desktop for revenue and privacy positioning |
EXECUTION & ITERATION
Authentication, workflow engine, visual editor, 10 core nodes, basic dashboard, PouchDB integration for local storage
Workflow versioning system, loop/iteration support, SDK for node development, templates (initial 6)
Electron desktop app, license key system (LemonSqueezy), template expansion to 36, professional landing page
4-tier AI fallback system, Phi-3 local model integration, request caching layer, auto-save functionality
| Stage | Validation Method | Outcome |
|---|---|---|
| Prototype | Internal dogfooding with 5 workflows | Identified 3 critical UX issues in node configuration |
| Alpha | 3 beta users (clinic, store, cooperative) | Confirmed offline-first value; added auto-save after feedback |
| Beta | Template adoption tracking | 80% of users started from templates; expanded library |
| Feedback | Response |
|---|---|
| "I keep losing work when my browser crashes" | Added auto-save with 3-second debounce and visual status |
| "I don't know which AI is running" | Added status indicators showing active AI provider |
| "Setting up integrations takes too long" | Expanded templates from 6 to 36 across 9 categories |
| "I'm scared to change my workflow" | Built versioning with preview and one-click rollback |
node-llama-cpp required ESM modules while the codebase was CommonJS. Migrated entire backend to ESM, updating all imports and build configuration. Result: Phi-3 local AI now works seamlessly.
PouchDB sync could create conflicts when the same workflow was edited offline on multiple devices. Implemented last-write-wins with conflict detection and user notification. Trade-off: Accepted potential data loss edge case vs complexity of full CRDT implementation.
OUTCOMES & IMPACT
Time to first workflow
(with templates)
AI uptime achieved
(4-tier fallback)
Offline functionality
Core features work offline
Template adoption
Users start from templates
| Issue | Learning |
|---|---|
| Initial 6 templates weren't enough | Users expected their specific use case to be covered; expanded to 36 |
| Auto-save wasn't in MVP | Lost user work during testing; should have been P0, not Phase 4 |
| Phi-3 local model is 2.4GB | Download size deterred some users; considering smaller models |
KEY TAKEAWAYS
The assumption that "everyone has internet" ignores SMBs in rural areas, privacy-conscious users, and anyone who's lost work to connectivity issues. Building for offline-first from day one shaped every architectural decision—and created defensible positioning.
Every API key, every configuration step, every account creation is a drop-off point. The Phi-3 local model—download and run, no keys—converted users who'd abandoned Zapier at the API key step.
Users fear breaking things. The anxiety of "what if I mess up my working automation" prevents experimentation. One-click rollback removes that fear entirely. No major competitor offers this.
I initially treated templates as marketing collateral. They're actually the primary user experience. 80% of users never build from scratch. Template quality and coverage directly drive adoption.
I treated it as a Phase 4 polish feature. I watched a beta user lose 30 minutes of work to a browser crash. Auto-save is core functionality, not a nice-to-have.
The initial template set was too narrow. Users expected their specific use case to be covered. Expanding to 36 templates significantly improved activation rates.
The 2.4GB Phi-3 download works well but creates friction. I'd investigate smaller quantized models (500MB-1GB) as a fast-start option with the larger model as an upgrade.
I delayed telemetry implementation; earlier data on template usage, node popularity, and drop-off points would have informed prioritization faster.