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Engineering & product playbooks
Hands-on playbooks, decision frameworks, and case studies from the team building AI-native products at CodeNicely.
Your Legacy System Isn't the Problem. Your Data Is.
Most legacy modernization projects fail for a reason that has nothing to do with the software being replaced. The real constraint sits in the data you're planning to migrate on day one.
How Vahak Onboarded 800K Trucks Without Burning the Database
Vahak's onboarding pipeline scaled from hundreds to hundreds of thousands of trucks without breaking live matching queries. The unlock wasn't sharding or caching — it was separating write and read paths at the data model level.
5 Mistakes Teams Make When Digitizing a Transport Marketplace
Freight marketplace platforms rarely fail because of matching algorithms. They fail because the supply side was modeled as passive inventory. Here are the five mistakes we see mid-sized brokerages make when digitizing, and how to recover from each.
What Is RAG? Stop Letting Your LLM Hallucinate Your Own Data
Your chatbot invents answers about your own product because the LLM has never seen your docs. Here's what RAG actually does, why retrieval quality matters more than model choice, and when fine-tuning is the wrong fix.
Rate-Limit LLM API Calls Across Workers Without a Queue
Your OpenAI feature works fine on one worker and dies on four. Here's how to build a distributed token-bucket limiter in Redis with an atomic Lua script — no queue, no broker, no rewrites.
Best Digital Transformation Companies in India for SMBs
A practitioner's guide for Indian SMB owners deciding who to trust with their first serious digitization project. Compares Tier-1 IT firms, boutique agencies, freelancers, and AI-first product studios — with honest tradeoffs for each.
Microservices vs. Modular Monolith: Pick the Right Architecture
Your monolith is slow to deploy and teams are stepping on each other. Before you split into microservices, here's how to tell if your real problem is coupling, pipeline design, or actual scale — and which architecture solves which.
Questions to Ask Before Hiring an AI Fintech Dev Partner
Most fintech RFPs get won by the vendor with the slickest deck, not the one that has actually reconciled a failed disbursement at 2 AM. Here are the questions that expose the difference in under an hour.
AI Feature Flags Cheatsheet: What to Gate, Rollback, and Monitor
Standard feature flags gate users. AI features need a second layer that gates model behavior — version, prompt, embedding schema, output quality. Here's the reference cheatsheet.
Celery vs. Temporal: Pick the Right Workflow Engine for AI Jobs
Celery loses in-flight state when a worker dies mid-chain — and no amount of logging fixes that. Here's an honest head-to-head with Temporal for teams running multi-step LLM and document-parsing pipelines in production.
How to Migrate a Multi-Tenant SaaS Schema Without Breaking Tenants
Every migration guide assumes all consumers of the database deploy at the same time. In a multi-tenant SaaS, they don't. Here's the playbook we run when tenants are on different release tracks and a single ALTER TABLE would break half your customers.
5 Mistakes Teams Make When Automating Loan Collections
Most collections automation projects ship faster outreach without modeling why borrowers actually miss payments. Here are the five mistakes we see NBFCs and digital lenders make — and how to recover before roll rates get worse.