Where AI Automation Fits in Operations
AI automation is most useful when the task is bounded, reviewable, and connected to a clear human workflow.
SutharTech Blog
Insights on product engineering and building dependable digital experiences.
Choosing a Scalable Data Model
A data model should make current rules explicit while leaving a clear path for likely changes.
Measuring Useful Product Analytics
Useful analytics answer specific product questions while respecting the people who use the service.
Evaluating an AI Feature before Launch
Test representative cases and define where human review remains necessary.
Keeping Human Review in AI Workflows
Make uncertain outputs visible and place review where decisions carry impact.
Defining Data Retention Rules
Collect only useful information and document how long each class is kept.
Building a Reliable Data Import
Validate source files, report rejected rows, and make retries safe.
Monitoring Data Quality over Time
Freshness, completeness, and consistency checks catch drift before it spreads.
Using Analytics to Answer Product Questions
Start from a decision and instrument only the events needed to inform it.