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Learn before you build
What software really costs, how long it takes, how to pick a partner, and how to put AI tools to work — for startups, SMBs and enterprises.
How GimBooks Served 3M Users Without a Broken Ledger
A teardown of the inflection point most accounting SaaS hit between 50K and 500K users — where ledger drift, reconciliation failures, and AI categorization errors look like three problems but are actually one. Here is what we learned shipping through it with GimBooks.
5 Mistakes We Made Shipping AI to a Live Pharmacy Marketplace
A field-level post-mortem on what breaks when AI substitution, routing, and recommendation features hit a real e-pharmacy catalog. Five specific mistakes, the symptoms you'll see in production, and how to recover without rolling everything back.
How KarroFin Scaled AI Credit Scoring Without Killing Approval Rates
KarroFin's credit model wasn't broken. No alerts, no errors, no engineering fires. But approval rates were quietly compressing at scale — and the fix wasn't where the data science team was looking.
5 Mistakes We See Teams Make Shipping AI to Thin-File Users
Most thin-file AI lending models don't fail because the architecture is wrong. They fail because the team never audited what happens after the first batch of rejections starts retraining the model. Here are the five failure modes we see most often.
How Vahak Matched 800K Trucks Without a Recommendation Collapse
A deep dive into the architectural decisions behind Vahak's freight matching engine — and the counterintuitive reason match quality got worse as supply grew. How feedback loop pollution, not data sparsity, became the real enemy at marketplace scale.
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