The breakneck pace of generative AI development has created a paradoxical challenge for modern CTOs. Every week, a new model—often more powerful, cheaper, or faster than its predecessor—grabs the industry spotlight. For product teams, the temptation to immediately integrate these models into their stacks is intense. However, the shiny allure of a new model often masks a fundamental technical reality: a model that performs brilliantly in a controlled demo can catastrophically degrade a product in the wild.

At Inc42’s The CTO Summit 2026, held last week, a clear consensus emerged among leaders from some of India’s most prominent tech giants, including Swiggy, Rapido, Razorpay, ShareChat, Shadowfax, and Meesho. The engineering question is no longer "which model should we use?" Instead, it has shifted to a more rigorous, operational inquiry: "How do we prove that a model change is safe, reliable, and worth the investment?"

This transition marks a pivotal shift in the software development lifecycle (SDLC). As AI systems move from experimental prototypes to the backbone of customer-facing operations, "evals"—systematic, automated evaluations—have become the ultimate differentiator in AI strategy.

The Death of Model Monogamy: A Chronology of Evals

The industry is currently witnessing a transition from "model-centric" development to "evaluation-centric" development.

• Phase 1: The Honeymoon (2023–2024): Companies experimented with single-provider reliance, often hard-coding their workflows to specific models. As long as the models were "good enough," this was acceptable.
• Phase 2: The Pain of Migration (2024–2025): Early adopters like Swiggy realized the cost of this rigidity. When providers faced capacity constraints or performance dips, companies found themselves trapped in a "vendor lock-in" scenario, with migrations taking weeks or months.
• Phase 3: The Operational Maturity (2026–Present): Today, forward-thinking CTOs are treating AI models as replaceable components. By building robust evaluation infrastructure, these companies can now swap models with the same ease as updating a library, provided the new model passes the internal "eval" suite.

Why Evals Are Now Critical Infrastructure

Swiggy’s experience serves as a cautionary tale for the industry. CTO Madhusudhan Rao noted that an early contact-center agent was so tightly coupled to a single model that a provider outage resulted in a month-long migration ordeal. "Treat the model as replaceable," Rao advised. "Invest in the workflow, the evals."

This sentiment is echoed across the sector. Nitin Jain, CTO of ShareChat & Moj, emphasizes that evaluations act as the "internal gatekeeper" for quality, while A/B testing acts as the "external pulse check." According to Jain, these are not separate disciplines but two sides of the same coin, designed to measure whether latency, cost, and quality improvements manifest without disrupting the user experience.

The Shadowfax Standard

Shadowfax, under the leadership of co-founder and CTO Vaibhav Khandelwal, offers a blueprint for production-grade evaluation. Currently on their third model iteration for a delivery-partner support bot, the team uses observability tools to compare iterations in real-time. The goal is not to find a model that scores high on generic benchmarks, but one that excels on the product’s specific "operating measures."

For instance, a support bot that is technically accurate but repeats itself is a failure. A commerce assistant that misinterprets a complex regional request is equally useless. By identifying these "failure categories," engineering teams can create targeted tests that ensure a model change actually improves the user journey.

Supporting Data: The Economics of Automated Quality

One of the most compelling arguments for building in-house eval infrastructure is cost-efficiency at scale. Zomato’s "Gavel" infrastructure provides a window into this reality. By comparing automated judges against human reviewers, the company found that automated review costs are a fraction of the cost of human oversight.

• Cost Efficiency: While human review for a voicebot call might cost ₹450, an automated judge can perform the same check for approximately ₹10.
• Accuracy at Scale: As of July 2026, Gavel had evaluated over one million items, with automated judges reaching human-level parity on 90% of critical failure modes.

However, these figures come with a caveat: automated judges are not a complete replacement for human judgment. They are a force multiplier. They allow teams to widen their coverage while human reviewers define, refine, and validate the quality bar.

Implications: The Loop of Action

For an AI system to be truly production-ready, it must exist in a closed loop. The workflow should look like this:

1. Real User Output: Capture raw interactions from the field.
2. Failure Classification: Categorize these into "business-critical" errors.
3. Targeted Testing: Create automated evals that specifically target those failure modes.
4. Decision Gate: Use the results to either promote, tweak, or roll back the model.

If a model cannot be measured against this loop, it should not be in production. As Anand Jain of Meesho pointed out, the core challenge for CTOs is defining what "better" means for their specific workflows and determining the exact threshold of regression that will trigger a rejection.

Market Pulse: Anthropic’s Big Move

While Indian tech firms are refining their internal evals, the global market is bracing for a seismic shift. Anthropic is reportedly moving toward a pre-Thanksgiving IPO with a valuation potentially nearing $2 trillion.

The company’s dual strategy—deepening its footprint in regulated industries like healthcare and finance while simultaneously pushing for industry-wide evaluation standards—highlights a growing trend: safety and reliability are no longer "features," they are the primary products. With reports of a $42 billion chip-lease financing deal with Broadcom, Anthropic is signaling that the era of "frontier models" is shifting into an era of "frontier infrastructure."

Startup Spotlight: ByteAsk and the Reliability Gap

While most of the industry focuses on high-level language models, ByteAsk, founded in 2026 by IIT-Delhi alumni, is tackling a different beast: mission-critical C/C++ development.

In sectors like aerospace, defense, and medical technology, code generation speed is secondary to safety and reliability. Conventional AI tools often struggle with the rigorous constraints of these environments. ByteAsk is filling this void by allowing companies to deploy AI coding agents on-premise, ensuring that sensitive intellectual property never leaves the firm’s infrastructure. By focusing on the "performance-critical" segment of the market—estimated at $11 billion—ByteAsk is demonstrating that the future of AI is not just about general intelligence, but about domain-specific, high-reliability engineering.

The CTO’s Toolkit: The "Gate" Method

As organizations grapple with these changes, the need for standardized human-in-the-loop processes remains vital. Yash Varyani, CTO and co-founder of Drizz, utilizes a structured five-gate prompt for reviewing pull requests (PRs) that has become popular among engineering leaders.

By evaluating PRs through gates focused on Problem, Production, Architecture, Code Health, and Ship Readiness, Varyani forces a holistic review that goes beyond simple syntax. This methodical approach ensures that even when AI suggests code, the human overseer maintains a rigorous standard for what is actually deployed to production.

Conclusion: The Competitive Edge

As we look toward the second half of 2026, it is clear that the AI arms race has entered a new chapter. The winners will not necessarily be those who have access to the largest models, but those who have built the most robust "eval" pipelines.

The ability to swap models, measure performance with granular precision, and act on that data in real-time is the new mandate for the modern enterprise. In this environment, "moving fast" is no longer about speed of deployment; it is about the speed at which a team can identify a failure, learn from it, and safely pivot to a better, more cost-effective solution. The firms that master this cycle will set the pace for the next decade of digital transformation.