The most important AI trend in Indian tech right now isn't a new model — it's how companies test them. At Inc42's CTO Summit 2026 last week, engineering leaders from Swiggy, Rapido, Razorpay, ShareChat, Shadowfax and Meesho described building evaluations — "evals" — and continuous monitoring into the AI model lifecycle, Inc42 reported on October 7.
Evals are systematic tests that measure how well an AI model performs a specific task. The argument from the unicorns: a new model can look brilliant in a demo and still make a product worse — cheaper but slower, passing offline tests but failing with real customers, or behaving differently once traffic and context change. The engineering question is shifting from "which model should we use?" to "how do we prove a model change is safe and worth making?"
As platforms scale, evals are becoming a key differentiator in AI strategy — model choice is now a continuing production decision, and evaluation is the evidence base for making it. The bar is higher in India: AI products must work on budget smartphones, patchy networks and multiple languages, all failure modes that only rigorous evals catch.
The gap is the opportunity. A ServiceNow AI maturity assessment cited this week found that only 22% of Indian enterprises have AI testing, auditing and risk-assessment processes in place — even as CIOs and CISOs move from small pilots to organisation-wide deployments.
Why it matters for founders
Evals are the unsexy moat. Any founder can call an API; far fewer can prove their AI system is safe, reliable and improving. If you're building AI products, investing in eval infrastructure now — test suites, monitoring, rollback discipline — is what separates a demo from a production system enterprise customers will trust. And with only 22% of enterprises having testing processes in place, eval tooling itself is a wide-open product category.