Built by a team that spent years on the wrong side of intent data
We ran RevOps and sales engineering at B2B SaaS companies. We know what it feels like to call accounts that look warm in the CRM but went cold three weeks ago. Highperformr AI exists to fix that.
Why we built this
For years, revenue teams at B2B companies have had the same problem: they know which accounts they want to close but not which ones are actually ready to buy right now. Standard CRM data tells you what happened in the last conversation. It does not tell you whether the account started evaluating a competitor last Tuesday, posted seven new engineering roles focused on the pain point your product solves, or went silent on all the channels they used to engage on.
We spent time at companies that tried to solve this with manual research, with expensive intent data subscriptions that needed a full-time analyst to interpret, and with spreadsheets that were stale by the time they reached a rep. None of those approaches scaled, and all of them required work that reps should not be doing.
The core insight behind Highperformr AI is simple: modern AI models can process far more account-level signals than any analyst team, can weight them against your historical conversion data, and can write the output directly into the tools your reps already use. We built the infrastructure to do exactly that and made it accessible without requiring a data science team to configure it.
We are a small team based in Chennai, working closely with a handful of pilot customers. We have chosen to grow slowly and get the signal quality right before scaling. Every account on our platform is treated as a configuration problem we own, not a data point we hand back to you to interpret.
The team
A small group with backgrounds across revenue operations, applied machine learning, and B2B SaaS.
Srivatsan Venkatesan is a revenue and go-to-market builder who has spent years building sales-tech products for B2B teams. As CEO and Co-Founder, he leads Highperformr AI's go-to-market and brings a deep understanding of intent-signal plumbing from the buyer side of the problem.
Karthik Rajan builds backend systems and data pipelines for high-throughput signal ingestion and scoring. After building this kind of infrastructure at independent software companies, he now owns Highperformr AI's scoring architecture and ingestion pipeline as CTO and Co-Founder.
Divya Krishnamurthy works across sales operations and revenue-team onboarding, having guided early B2B teams through their first intent-data programs in the Chennai and Bangalore tech ecosystem. As Head of Customer Success, she owns pilot onboarding and focuses on helping sales teams adopt new signals and use them in their day-to-day work.
Rohan Mathur works on product and UX for sales-automation tools, with years translating rep workflow pain into data-layer product decisions at a Bangalore-based SaaS company. As VP Product, he leads product direction and design with a focus on the gap between what data teams build and what reps will actually adopt.
How we work
The principles that guide how we build the product and work with customers.
We deliberately keep the pilot cohort small so we can own the configuration and validation for every customer team. A large customer base with mediocre signal accuracy is not a business we want to build.
Every design decision gets tested against this: does it require a rep to log in somewhere new, export a spreadsheet, or check another dashboard? If yes, it goes back to the drawing board.
We include low-confidence flags in the scoring output. A rep who acts on a false positive loses trust fast. We would rather flag uncertainty than over-claim on an account signal.
Want to work with us?
We are a small team working closely with pilot customers. If our approach to intent data resonates with your sales team's actual pain, we would like to talk.