Matthew Kayser
Modern AI systems can now make over 300,000 calls a month, handling honest customer conversations with human-like fluency.
But behind that smooth experience lies a hidden challenge to reliability. When a single line of code fails, hundreds of conversations can break.
At Toma (YC W24), one of the fastest-growing AI startups in the automotive space, this challenge is solved by engineers like Saisrikar Surisetti, the company’s Founding Engineer.
He helped design the systems that power these large-scale voice operations, ensuring they run efficiently, consistently, and safely.
His work demonstrates that reliability, rather than just innovation, is the proper foundation of AI progress.
Early Discipline and Systems Thinking
Reliability begins with mindset. For Saisrikar, that mindset was shaped early through competitive robotics, professional gaming, and ice hockey disciplines that demand focus, precision, and resilience.
As the President of Churchill Robotics, he led a 150-member team with an impressive 95% win rate and 20+ awards, learning how to balance speed with control, the same balance required when scaling AI systems.
Gaming sharpened his ability to make fast, data-driven decisions under pressure, while ice hockey taught him teamwork and endurance.
These experiences gave him a systems-oriented way of thinking: anticipate problems before they occur, plan for every possible failure, and build frameworks that don’t just work. They adapt.
The approach became the backbone of his engineering philosophy and prepared him to take on the complex, high-stakes world of large-scale AI infrastructure at Toma.
Founding Engineer at Toma
Toma (YC W24) is on a mission to revolutionise the way car dealerships engage with customers. The company’s AI voice agents handle outbound calls, appointment bookings, and customer follow-ups, automating what was once hours of manual work.
When Saisrikar joined as Engineer #1, he faced the unique challenge of building reliable systems from scratch. Each dealership has its own data management process, so the AI had to adapt seamlessly to thousands of unique workflows.
He developed the core features that made this possible: outbound calling automation, CRM and DMS integrations, and intelligent follow-up tracking. His work unlocked AI access across multiple dealership departments, expanding Toma’s impact far beyond service teams.
Saisrikar also helped maintain Toma’s in-house orchestration system, which coordinates millions of AI interactions while keeping latency remarkably low among the best in the industry. The architecture enables Toma’s AI to respond instantly, without delay, even at massive scale.
For readers, the lesson is clear: great AI isn’t just about creativity — it’s about consistent, scalable performance.
Why Reliability Matters More Than Glamour?
In AI, reliability often goes unnoticed, yet it is the key to success. An AI that sounds impressive means little if it crashes under pressure. That’s why Saisrikar focuses on architecture before aesthetics.
He spends much of his time refining the systems that keep AI stable, fixing memory leaks, removing race conditions, and designing multi-threaded tests to ensure every call runs smoothly.
These behind-the-scenes tasks might not make headlines, but they’re the reason thousands of calls can happen daily without error.
For industries adopting AI, a vital takeaway is that innovation must be built on consistency. Whether it’s a chatbot, a virtual assistant, or a voice agent, the product’s value depends on how predictably it performs.
Saisrikar’s work at Toma proves that engineering excellence isn’t about prioritising flashy things. It’s about doing the fundamentals right.
Where Reliability Meets Creativity
Saisrikar’s creativity shows up where most would see constraints. His first major project, the AI Dealership Caller, was built using TypeScript, Twilio, ElevenLabs, and Next.js. It was designed to handle thousands of calls simultaneously from a single local machine, eliminating cloud costs.
The required deep load-balancing design and resource optimisation, proving that innovation doesn’t always need expensive infrastructure.
The project later evolved into Toma’s most extensive data campaign, where he developed scoring dashboards that measured how quickly dealerships responded to customers and how competitive their prices were.
The system turned voice call data into actionable insights, helping dealers improve both sales and service.
The key takeaway? Creativity in engineering isn’t just about new ideas. It’s about finding more innovative, simpler solutions.
By focusing on performance and cost efficiency, Saisrikar showed how AI can be both powerful and practical for businesses of any size.
Competitive Edge
Saisrikar’s technical precision also comes from his academic and analytical background.
He placed first in the Jane Street Estimathon at the University of Waterloo. This competition blends logic, probability, and fast problem-solving, the same skills needed to diagnose complex AI issues.
During his short time at Waterloo, he also completed several advanced mathematics and computer science courses, which gave him a deep understanding of algorithms and optimisation.
These skills now shape his work at Toma, where he combines mathematical rigour with engineering intuition, balancing creative design with exact reliability.
It’s this mix of analysis and adaptability that allows him to engineer systems that scale effortlessly without losing stability.
The Engineer Behind the Emotion
In a world chasing flashy AI breakthroughs, reliability remains underrated, yet it’s the foundation that makes everything else possible.
Through his work at Toma, Saisrikar Surisetti has demonstrated that dependable systems are key to making AI trustworthy and scalable.
His journey, from robotics and gaming to engineering at one of the most promising startups, reminds us that the future of AI depends on people who value stability as much as creativity.
He’s not just helping AI sound human, he’s making sure it acts dependably, every time. And that reliability may be the most human quality of all.