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Engineering & product playbooks
Hands-on playbooks, decision frameworks, and case studies from the team building AI-native products at CodeNicely.
How GimBooks Served 3M Users Without Breaking GST Logic
A walkthrough of how the GimBooks accounting SaaS handled GST edge cases at scale by treating compliance as a state machine, not a calculation library. The lesson generalizes to any fintech whose rule logic works at 50K users but silently breaks at 500K.
How to Hire an AI Development Partner in India
Most Indian AI vendors demo beautifully on clean data. Fewer have kept models accurate against GST rule changes, UPI schema drift, and 2GB-RAM Android users. Here's how to tell them apart before you sign.
What Is a BFF? Why Your Mobile App Deserves Its Own API
A shared API for web and mobile sounds efficient until your mobile team is making four round-trips to render one screen. Here's why the Backend for Frontend pattern is really about org structure, not network hops.
Your AI Pilot Succeeded. That's Why It Will Never Scale.
A successful AI pilot is often evidence of a controlled exception to your operating environment, not proof the system works. Here's why the better your pilot performed, the more dangerous it is as a business case for full deployment.
AI Model Latency Budgets: A Cheatsheet for Product Teams
Latency tolerance isn't a property of your model — it's a property of where the result appears in the user's workflow. A reference for setting defensible p99 targets for AI features in production SaaS.
Feature Store on a Budget: Serve ML Features from Postgres
You don't need Feast, Tecton, or a Redis tier to stop training-serving skew. A properly designed append-only feature table in the Postgres you already run will fix it — here's the exact schema, queries, and gotchas.
FastAPI vs. Django for AI Model Serving: Pick the Right One
Your p95 latency isn't creeping past 800ms because Django is slow. It's creeping up because a synchronous, GIL-bound model call is blocking your event loop — and FastAPI won't fix that on its own. Here's how to actually choose.
Questions to Ask Before Hiring an AI Logistics Dev Partner
Most AI logistics vendors can demo a dispatch dashboard. Far fewer can model lane exclusions, HOS rules, and carrier telemetry dropouts. Here are 15 questions that expose the difference before you sign.
How to Migrate a Live E-Commerce Catalog to AI Search
Your semantic search prototype beats keyword search in offline eval. That's not the hard part. The hard part is rolling it out on a live catalog with thin product data without watching conversion rate slide for two weeks before anyone catches it.
5 Mistakes Teams Make When Automating Pharmacy Operations with AI
Most pharmacy AI automation rollouts fail not because the model is inaccurate, but because it was trained on staff workarounds instead of the real dispensing workflow. Here are the five mistakes we see most often, with the symptoms and how to recover.
How to Hire an AI Development Partner in the UK
Hiring an AI development partner in the UK isn't about portfolio size or day rate. It's about whether the vendor can produce a UK GDPR Article 28 controller-processor mapping on request — and seven other concrete tests that separate production shops from landing-page operations.
Best AI Development Companies in Australia for SMBs
Most Australian SMBs evaluating AI partners pick between agencies that bolt GPT-4 onto a dashboard and offshore shops with no accountability. Here's an honest landscape of the vendor categories that actually fit a sub-$300K AUD AI build.