When I started working on TaskScout.ai, my goal was simple: make maintenance smarter. Multi-site businesses like restaurants, hotels, and gas stations often rely on outdated systems or messy spreadsheets to manage vendors, track assets, and prevent downtime. That chaos leads to high costs and unhappy customers.
As an engineer, I wanted to tackle this problem with a Computerized Maintenance Management System (CMMS) powered by AI and automation. But the journey from idea → working SaaS platform hasn’t been easy. Here are a few lessons I’ve learned so far:
- Don’t over-engineer the MVP
When I began, I wanted predictive analytics, IoT integrations, and a beautiful dashboard all at once. Big mistake. The better approach was:
Start with ticketing + vendor management (the core pain point).
Add automation features once customers actually used it.
Save predictive maintenance for v2.
The MVP should solve one problem really well. Everything else can wait.
- AI is powerful, but context matters
It’s easy to say “AI will fix maintenance,” but AI without good data is useless. For TaskScout, that meant:
Normalizing vendor response times.
Tracking mean time to repair (MTTR).
Logging failure types consistently.
Only after clean data collection could we apply ML to suggest preventive schedules or flag high-risk equipment.
- Marketing is as hard as coding
I thought building the app would be the hardest part. Nope. Getting visibility is just as tough. A few things that have helped:
Writing content (like this!) about CMMS, predictive maintenance, SaaS building.
Posting on LinkedIn and industry groups.
Setting up a small referral program for consultants and facility managers.
As devs, we sometimes think “if I build it, they will come.” Reality: you need to ship code + ship marketing together.
- Tools I’ve used along the way
Flask + MongoDB for the backend.
Docker + Compose for local dev + team collaboration.
Stripe + Rewardful for payments and early affiliate setup.
Gemini & GPT for blog drafts and internal automation.
Final Thoughts
Building TaskScout.ai has been both exciting and overwhelming. If you’re working on a SaaS product:
Focus on solving one painful problem.
Collect clean data before layering AI.
Don’t ignore marketing — it matters as much as your code.
I’ll be sharing more about the technical side (our ML approach to predictive maintenance, containerization tips, and scaling MongoDB) in future posts.
👉 If you’re curious about what I’m building, check out taskscout.ai
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