Bhargava's Profound AI Rep
Bhargava's view on scaling AI products shows how he thinks about the relationship between customer proximity and production reliability.
Bhargava T
Edges
Building AI Reliability Infrastructure
Bhargava builds the observability and eval layers that make AI products reliable enough to operate in high-stakes environments. His focus is on the unglamorous infrastructure: unified monitoring, threshold alerting, and the feedback loops that catch failures before they become downtime. He did this at SquadStack, where he unified fragmented telemetry across seven voice AI systems into a single dashboard and drove campaign reliability above 95%. He did it again at Keka, where he built the eval and A/B testing framework that took a GenAI chatbot from demo to production grade. The mechanism is consistent: instrument the system first, make failures visible, then iterate on the product layer with confidence.
Spotted in 2 Stories
Scaling AI Agents Through Iteration
Bhargava scales AI agent deployments by running structured iteration loops grounded in real customer signal. He audits calls, identifies the gaps the model cannot see, and feeds that back into the product before pushing for volume. At SquadStack with Kotak Bank, he ran phased iteration across zero-to-one, one-to-ten, and ten-to-hundred stages, auditing calls at each phase to find the objections the agent was missing. At Keka, he applied the same discipline to a GenAI chatbot, using A/B evals and weekly stakeholder cadences to move from demo to production. The result in both cases was a product that could handle real-world load because the failure modes had been found and fixed before scale was attempted.
Spotted in 2 Stories
Driving Stakeholder Adoption of AI Products
Bhargava earns stakeholder trust for AI products by showing metrics before asking for commitment. He demonstrates what the product does well, names what it does not, and builds conviction through visibility and cadence. At Keka, he aligned the customer support leader on the GenAI chatbot by running a transparent demo, then maintained weekly and bi-weekly cadences until the team committed resources. At SquadStack, he kept the engineering team aligned on the observability build by surfacing the builders' daily complaints until the severity of the problem made the investment obvious. In both cases, the adoption came from making the problem and the solution visible at the same time.
Spotted in 2 Stories
Bhargava T
About Me
Bhargava T is an AI Product Manager specializing in voice AI and conversational agent platforms, with a focus on the BFSI sector across lending and insurance.
He currently builds the platform layer at SquadStack.ai, owning enterprise client onboarding for voice AI deployments. His work spans observability infrastructure, reusable prompt architecture, and the reliability systems that keep AI agents performing at scale. He has reduced client onboarding time from seven weeks to four and driven campaign reliability to above 95%.
At Keka HR, he shipped a GenAI support chatbot that deflected 53% of support tickets in its first quarter, handling two thousand queries per day, and built a product usage dashboard that gave account managers visibility into at-risk customers, reducing churn by 3% quarter-on-quarter.
At Wisemonk, he built India's payroll system from the ground up for an EOR platform and implemented a product-led growth strategy that improved lead qualification by 27% within the first month.
Bhargava T
You can't skip the zero-to-one phase to get to scale. The engine has to be right first, and the only way to get it right is to be close to the customer before you push for volume.ā
Bhargava T
On scaling AI products