Engineering Blog — Page 3
Practical notes on AI systems, product engineering, architecture, and shipping reliable software.
Business Process Improvement with Software: Where to Start for Real ROI
A practical BPI playbook: map processes, find bottlenecks, choose automation vs productization, measure ROI, and avoid digitizing broken workflows.
Database Migration Guide: Zero-Downtime Patterns Businesses Need
Practical database migration patterns—expand/contract, dual writes, backfills, CDC, and cutover checklists that protect revenue systems.
Legacy System Modernization: Improve the Business Without Freezing Features
How to modernize legacy systems while shipping product: modularization, anti-corruption layers, risk scoring, and capacity allocation for business improvement.
Cloud Migration for Businesses: Benefits, Risks, and a Sensible Approach
What cloud migration really improves—and what it does not. Rehost, replatform, refactor choices, cost control, security shared responsibility, and anti-patterns.
Software Migration Strategy: A Practical Roadmap Without the Drama
A step-by-step migration strategy: discovery, strangler patterns, dual-run, data cutover, rollback, and program governance for mid-market and product companies.
Why Companies Migrate Software Projects: 12 Real Business Triggers
The real reasons organizations migrate systems—cost, risk, talent, compliance, product speed—and how to tell a necessary migration from a resume-driven rewrite.
Building a Moat for AI Products: Data, Workflow, Distribution, and Trust
Why model access is not a moat—and how AI product companies build durable advantage with proprietary data loops, workflow depth, distribution, and trust architecture.
10 Ways to Improve Conversion on AI Product Landing Pages and Trials
Growth improvements for AI products: clearer jobs-to-be-done, demo honesty, trial limits, trust UX, onboarding, and metrics that filter tire-kickers from buyers.
AI Product Strategy for Software Companies: Build, Buy, or Embed
Strategic choices for software businesses adding AI—net-new AI products, embedding AI into existing suites, partnering, and portfolio prioritization.
How to Improve AI Product Quality: Evals, Feedback Loops, and Human-in-the-Loop
A practical quality system for AI products—offline evals, online feedback, sampling, HITL design, and when to change prompts vs models vs data.