The Problem
Without capital, equipment decisions don't get made. Without guidance, the decisions that do get made are wrong — wrong machines, wrong sequence, wrong market.
Only 15% of Nigerian SMEs have any bank loan or line of credit. Most entrepreneurs start production planning with no financing, no supplier contacts, and no structured process. MachineLine addresses the information gap — giving every entrepreneur the same plan a consultant with 20 years of local market experience would produce.
Origin
This started with someone I know — not a market report.
My mum was thinking about pivoting to a different production line. Watching her navigate it made the gap obvious: she'd either have to go to China without knowing what to ask for, rely on intermediaries who might not give the right information, or spend weeks piecing together a plan from people with conflicting advice. That's the experience for most African entrepreneurs at this stage. MachineLine skips the middleman entirely.
What Was Built
Describe your business. Get a complete production plan in about 20 seconds.
- Machine list with production sequence, Africa-specific specs, and startup cost estimates — new and used prices on every item
- Sourcing panel across three channels: local markets and dealers, Alibaba with exact search terms and trusted suppliers, and secondhand platforms (Jiji, Tonaton, OLX Africa)
- Live rental rates, country-specific permits with fees and processing times, and space requirements — all pulled from the web in the same API call
- Risk flag system surfaces warnings automatically with specific mitigations — permit issues, power requirements, budget gaps
- PDF export and WhatsApp share so the plan goes where the entrepreneur actually works
Design Decisions
Built around capital constraints, not ideal conditions.
The new vs. used price toggle on every machine isn't a feature — it's a philosophy. African entrepreneurs don't plan around what the ideal setup costs. They plan around what they can actually afford right now. The tool respects that from the start.
Budget-aware logic is baked into the prompt: if someone enters under $500, the plan explicitly recommends a manual or artisanal setup and states exactly what budget they'd need to run a proper machine line. No false confidence about what's achievable.
Iterations
The architecture changed significantly before it was right.
- 6 API calls → 1 — the original build made separate calls for machines, sourcing, permits, space costs, and warnings. Collapsed into a single structured call with web search built in — faster, cheaper, and more coherent output.
- 30s function timeout — Netlify's default 10s limit caused 504 errors on complex plans. Extended to 30s with explicit error handling so users always know what happened.
- 2-step onboarding — the initial single-form approach was too heavy. Split into product + location first, then scale + budget, so users reach the plan faster with less friction.
- PDF sanitization — special characters in machine names caused corruption in the exported PDF. Built a sanitizeForPDF pass before rendering to catch and strip them.
Outcome
One AI call. What previously required a consultant.
The output was validated against a real Nigeria sachet water factory plan — 6,700 litres per day, $5,000–$15,000 budget. The AI plan matched what a local industry expert with 20 years of market knowledge would produce. Machine selection, production sequence, sourcing paths, permits, space costs — all in one call.