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If your business depends on spreadsheets, you already have a software problem. Not because spreadsheets are bad.
But because spreadsheets are usually a sign that your operations outgrew your systems. I see this constantly in growing businesses.
At first it’s manageable:
- one spreadsheet
- a few manual processes
- a couple of people coordinating everything.
Then the business grows. Suddenly:
- data lives in 7 different places
- people manually copy information between systems
- approvals happen in Telegram
- operations depend on specific employees
- mistakes become expensive
- nobody fully trusts the numbers anymore.
At this stage, the problem is no longer "organization". It becomes an operational scalability problem. And this is where many companies start looking at automation, AI, dashboards, or new tools.
But in reality, the first issue is usually much simpler: The business has no central operational system. Most operational chaos comes from disconnected workflows - not from a lack of effort.
One important thing though:
- Not every spreadsheet problem requires custom development.
- Sometimes a process should be simplified first.
- Sometimes the real bottleneck is operational structure, not technology.
- Good software should remove complexity - not digitize chaos.
Curious at what point other founders realized their business had outgrown spreadsheets.
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An 8-figure apparel brand reached out this week.
3,000+ SKUs. Growing 20% year on year. Inventory sitting at 2x where it should be.
Here's what they told me:
Reorder logic lives in one person's head.
Nothing is documented.
The business is exposed if that person is unavailable.
Hundreds of dead and slow-moving SKUs clogging the warehouse.
Cash locked up in units that stopped moving months ago.
No automated reporting. Everything pulled by hand. No reliable read on how collections are performing week to week.
Storewide promotions running without a clear view of which products actually need a markdown and which ones are being discounted unnecessarily.
This is what inventory looks like when growth outpaces the planning function.
Revenue is growing. The inventory system underneath it hasn't kept up.
The SKUs multiplied. The range sprawled. The reorder logic never got documented. The reporting never got automated.
And now the warehouse is full of the wrong things while the right things are running out.
Growth doesn't fix inventory problems...It exposes them.
The brands that scale cleanly are the ones that built the inventory intelligence layer before the complexity arrived...not after.
How far has your growth outpaced your inventory planning function?
I Take AI Slop and Turn It Into Product
At SanganakHQ, I've done this at least half a dozen times now. Same story every time.
Founder builds with Claude, Codex, Bolt, Replit, Lovable, v0. And it works. Real users, real payments, real data.
But under the hood? 50K, 100K+ lines of AI-generated code glued together. Functions duplicated dozens of times. Auth mixed into UI. Hardcoded secrets. No tests. Fix one bug, three more appear.
It works the way a house of cards works.
Then they message me: "We're scaling. Can you make this production-ready?"
So I audit every line. Separate what's solid (usually 20%) from what needs to be rearchitected. Build a day-by-day migration plan with exact pricing. Move it to a real stack: Next.js, Supabase, Vercel, proper auth, proper RLS, proper CI/CD.
The irony: the AI features are usually the cleanest part. The mess is everything around it. Business logic, payment flows, membership tiers. The stuff AI generated fast but never thought through.
Smart founders, solid ideas, AI got them to a working product. But "working" and "production-grade" are two very different things.
If your app runs on AI-generated code and you're about to scale, get it audited before you find out the hard way.
We built an automated reporting solution that connects an external HRM system with Power BI, even when there is no native integration available. The automation uses the HRM system API to extract the required data, structure it into a central data source, store it in Microsoft SharePoint, and automatically update Power BI reports.
This solution eliminated the need for manual Excel exports and daily file uploads, reduced reporting effort, and ensured that decision-makers always had access to up-to-date data. It is a strong fit for companies that use separate business tools but need centralized, reliable reporting in Power BI.
#automation
#ai automation
#n8n
automationAI AutomationMicrosoft Power BIAutomation EngineeringBusiness Workflow AutomationN8NOpenAI
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