About Vikas Goel

The career, briefly

I started my career in 1996 at CMC Ltd as a software developer, joining what was then the early wave of India's software services industry. From there, I moved through HCL/LexisNexis and then to Hughes Software Systems — companies where I learned how production systems get built, broken, and rebuilt at scale.

The next decade put me in the deep end of large-scale, mission-critical software — signalling and switching infrastructure at Aricent, distributed communications systems at Hughes, and rural network platforms as a Software Architect at VNL (Vihaan Networks). The work was foundational: building software that doesn't fail, doesn't lose data, and recovers gracefully under load. Those are exactly the same disciplines that enterprise SaaS platforms now demand.

In 2013 I joined blackNgreen as CTO, where I served for 13 years. The company's evolution mirrored my own — what started as a value-added services platform became a global enterprise SaaS company building customer-experience and AI products for large enterprises. blackNgreen's flagship OXM platform grew to serve 160+ enterprise customers reaching over 290 million end-users across Asia, the Middle East, Africa, and South America — a multi-product portfolio spanning customer experience, voice AI, and autonomous agent systems.

Along the way, in 2017, my team and I shipped MagicCall — a real-time voice changer for phone calls. It crossed 14 million downloads on Google Play, becoming one of the most-downloaded India-built voice apps on the store. The engineering challenge — speech in, transformed speech out, sub-second latency, on a real phone call, at consumer scale — turned out to be the same engineering shape that AI voice agents would demand a decade later. MagicCall is where we built that competence.

Education

I hold a B.Tech in Computer Science from HBTI Kanpur and an M.Tech in Artificial Intelligence from BITS Pilani (2020–2022). Going back to school for AI mid-career, while running engineering at blackNgreen, was the most useful thing I've done in the last decade. It forced me to rebuild my mental model of what software is — from deterministic systems with fixed rules to statistical systems whose behaviour emerges from training data and prompts.

Where I am now

In 2026 I left my CTO roles to build my own company, Thinkerwave AITech Private Limited. After 25 years of building other people's products at scale, I wanted to build my own — and to put the AI research I'd been doing on the side at the center of the work. Thinkerwave AITech is an Indian AI company with three things in motion.

First, ThinkerWave — the research arm, on self-evolving agent systems. It addresses a structural limitation I kept running into when building production AI: the system's capabilities can evolve, but its definition of what success looks like usually can't. Patent pending at the Indian Patent Office.

Second, Torbi — an AI-powered B2B prospecting product that reads buying signals across the web and matches them to a seller's entire catalog. Third, a creator-intelligence platform applying AI to the messy problem of matching the right creator to the right brand. The through-line across all three is the same engineering instinct: AI that is observable, recoverable, and honest about its limits.

Third, I write. Some of it shows up on this blog; the rest goes into the work itself.

Leading engineering at enterprise scale

The work has been as much about building organisations as building software. The product portfolio at blackNgreen today is the output of engineering teams across Gurgaon, Chennai, Dubai, and parts of Africa — recruited, structured, and developed over the last decade. Building enterprise SaaS that real businesses depend on isn't a single architect's discipline. It's a team sport.

Most of what I do day-to-day is the unglamorous work of making a large engineering organisation decide well, ship reliably, and recover gracefully. Hiring and growing senior engineers across timezones. Setting technical direction that survives generational platform shifts. Choosing which architectural debts to take, which to repay, and which to inherit silently from acquired teams. Building review and on-call cultures that don't burn people out at the scale of millions of users.

A few principles I rely on:

• Ship reliability before features. Enterprise-grade observability, idempotency, and recovery are the substrate AI sits on top of. Voice AI at scale is unforgiving — a single hallucination is a customer-impact event, not a chat-window inconvenience.
• Optimise for organisational decision quality, not individual brilliance. The best AI engineering happens in teams that share a high-quality eval discipline, not teams that have one star architect.
• Hire for engineering judgment over framework familiarity. Frameworks change every two years. Judgment compounds.
• Deeply technical CTO > generalist VP. I read code, design reviews, and run on-call. That's not a flex; it's the only way to lead engineering at this complexity honestly.
• Build enterprise SaaS the way customers actually consume it. Multi-tenant from day one. APIs over UIs. Audit trails everywhere. Predictable SLAs. The boring software disciplines that make the AI on top trustworthy.

How I think about building AI

Twenty-five years of shipping production software has left me skeptical of two things in AI conversations: confident predictions about AGI timelines, and confident dismissals of what current systems can do. The actual interesting question — to me — is what happens when you take an LLM seriously as an engineering substrate. What architectures hold up at scale? What evaluation regimes catch real failures and not just superficial ones? When does a system know what it doesn't know?

My bias is toward systems that are observable, recoverable, and honest about their limits. Voice AI in particular is unforgiving — there's no "please try again" loading spinner; the customer is on the line, the latency budget is in milliseconds, and a hallucination is a real-world consequence. That constraint shapes how I think about everything else.

Outside the work

I'm based in Gurgaon, India. I publish a YouTube channel called I AM THAT on philosophy and self-inquiry — a counterweight to the technical work, and quietly the place where I do my best thinking about what intelligence actually is.

Want to talk about voice AI, evolving evaluation criteria, or anything else? Reach out here or explore what I'm building.