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Engineering & product playbooks
Hands-on playbooks, decision frameworks, and case studies from the team building AI-native products at CodeNicely.
Detect Data Drift in a Scikit-learn Model Before Users Do
A runnable tutorial for adding Population Stability Index drift detection to a production scikit-learn classifier. Catch input shifts in a single interpretable number per feature, weeks before error rates move.
Pinecone vs. Weaviate vs. pgvector: Pick One for Production
Most SaaS teams pick the wrong vector store because benchmarks measure the wrong things. Here's how to choose between Pinecone, Weaviate, and pgvector based on the dimensions that actually matter under production load.
AI Retraining Triggers Cheatsheet: When and Why
A scannable reference for ML engineers running production models on calendar-based retraining schedules. Includes drift triggers, signal-type cadences, and a decision table for replacing your weekly cron.
Batch vs. Real-Time AI Inference: Pick the Right One
Most operational AI features don't need the freshest prediction — they need the most accurate one. Here's a decision framework for choosing between batch and real-time inference, written for logistics and operations teams watching their cloud bill climb.
Best AI Development Companies in India for SMBs
Most 'top AI companies in India' lists are either sponsored directories or rankings of firms that will never take an SMB call. Here's an honest breakdown of which vendor category actually fits a 50–500 person company with a real AI use case.
How to Retire a Legacy System Without Killing the Business
Replacing a business-critical legacy system isn't a code problem — it's a behavioral contract problem. Here's the playbook we use to retire 10-year-old systems while live traffic keeps flowing.
How GimBooks Kept AI Accurate Across 3M Downloads
When an AI bookkeeping feature works at 10K users but breaks at 500K, the instinct is to blame data volume. The GimBooks case study shows the real culprit is usually segment collapse — and the fix is architectural, not statistical.
Questions to Ask Before Hiring an AI SaaS Dev Partner
Most AI SaaS vendor pitches look identical until you ask the right questions. Here are the 15 a Series A founder should run through before signing — and the answers that separate operators from demo-builders.
Temporal Fusion vs. LSTM: Pick One for Demand Forecasting
Most TFT-vs-LSTM comparisons optimize for benchmark RMSE on clean data. Here's how the two architectures actually behave in production demand forecasting — covariates, retraining cadence, and serving cost at SKU scale.
Feature Stores Explained: Why Your AI Keeps Training on Lies
Your credit-scoring model passes every offline test, then degrades two weeks after deployment. The culprit isn't drift — it's that your training pipeline and your serving pipeline are computing features differently, and no one is enforcing they match.
AI Evaluation Metrics Cheatsheet: Pick the Right One
Most teams pick an AI evaluation metric because it was easy to instrument, then discover months later that the number looked fine while a key account churned. This cheatsheet maps the metrics to the business decisions they actually encode.
5 Mistakes Teams Make Shipping AI to an E-Pharmacy
Most e-pharmacy AI failures are not model accuracy problems. They are context, escalation, and output design problems that only surface once pharmacists and patients start ignoring the recommendations you spent six months building.