An Exclusive Interview with Vikrant Labde Co-founder & CTO of Turinton AI and Nikhil Ambekar Co-founder & CEO of Turinton AI
In this exclusive interview, Vikrant Labde, Co-founder and CTO, and Nikhil Ambekar, Co-founder and CEO of Turinton AI, discuss their vision for advancing intelligent automation.
They share insights into Turinton AI’s journey, its innovative approach to solving real-world challenges, and how their leadership is driving the next wave of AI-driven transformation.
What inspired you to start Turinton, and how did your shared backgrounds at Cuelogic influence your new venture?
At Cuelogic, we scaled to millions in revenue and got acquired by LTIMindtree, but we kept witnessing the same bottleneck—enterprises had data and business problems but couldn’t connect them because of how their systems were built.
After the acquisition, overseeing operational excellence at scale, I watched this constraint play out across Fortune 500 companies.
That’s when Vikrant and I realized the enterprise AI playbook was fundamentally broken. Eighteen months to deployment, massive data movement, constant failures.
We decided to build something different. Turinton exists because we knew we could deliver intelligence rapidly while respecting how enterprises actually operate. We’re solving a problem we’d lived inside for two decades.
How does Turinton demystify and simplify AI for large enterprises struggling with legacy systems and siloed data?
Traditional enterprise AI extracts data into lakes, moves it around, transforms it repeatedly, then runs models—that’s where projects die. We reversed that. We build reasoning engines that operate directly on your existing infrastructure—SAP, Oracle, manufacturing systems, supply chain networks.
Intelligence comes to your data, not the other way around. Your data stays under your control, locked within your governance frameworks. No extraction, no data lakes, no months of ETL delays.
This zero-data-movement architecture eliminates the infrastructure tax killing enterprise adoption and delivers business outcomes in 8 to 12 weeks instead of 18 months.
Nearly 90% of enterprise AI projects never reach production. Why does this happen, and how is Turinton tackling these barriers?
That 90% failure rate is a business problem, not a technology one. Projects fail because nobody owns the outcome, success gets measured by wrong metrics—model accuracy instead of ROI—timelines become endless political battles, and organizations never prepare people to use these systems.
We restructured everything. We identify the business owner from day one—the VP of Operations, Supply Chain Director, CFO. We define ROI upfront, deliver tangible business results in 8 to 12 week cycles, and embed change management throughout.
Stakeholders see real value and continue funding. That’s why 110+ of our use cases have reached sustained business results. It’s not luck; it’s a fundamentally different approach.
What are the most common misconceptions clients have about implementing AI, and how do you address them?
Common misconceptions: you need centralized data lakes first, bigger models solve everything, you must replace legacy systems, and AI is static after deployment. These are all wrong. You can derive intelligence from data where it exists using federated reasoning.
Specialized models trained on your specific data outperform large general models. Legacy systems contain decades of business logic—integrate and augment them, don’t replace.
AI requires continuous monitoring and retraining as conditions evolve. In discovery, we ask hard questions: where’s your data really located? What can’t change? What’s your actual risk tolerance? We build solutions that work in that reality, not theoretical future states.
As a young company, how do you attract top talent in AI and retain those who can help build a pioneering organization?
We attract people driven by genuine impact. A data scientist here sees their work deployed at Fortune 500 companies within weeks, not years. We maintain absolute clarity—we’re not pivoting based on funding trends. We hire people who challenge assumptions and think independently.
Critically, we have founders who’ve built at scale before, who understand enterprise complexity from inside, who can mentor through ambiguity.
The best engineers stay when three things happen: meaningful problems that matter, continuous growth through challenges, and leadership that actually understands the space. We deliver all three intentionally, which is why our team stays engaged and committed.
How do you envision the future of agentic AI platforms, and what role will Turinton play over the next five years?
Most discussions focus on technical autonomy—how much systems can do without humans. The real question is how to build autonomous systems that understand business constraints, explain reasoning transparently, and know when to escalate.
An agent making supply chain decisions needs to understand supplier relationships, compliance, risk tolerance, and priorities holistically.
Over five years, we’re building agents operating directly inside enterprise infrastructure, reasoning without extraction, delivering full explainability. We’re creating industry-specific reasoning engines for manufacturing, pharma, logistics, and finance.
We’re architecting hybrid human-agent workflows where governance and explainability are foundational. Enterprises adopt autonomous AI at the pace they trust it.
What advice would you give to Indian tech founders tackling deep-tech problems for global enterprises?
Solve problems you intimately understand rather than chasing Silicon Valley narratives. We built Turinton after two decades in enterprise technology, knowing with certainty this problem was real.
Second, control your destiny by building to own your future, not toward acquisition—that changes everything about strategy and hiring.
Third, build products and IP, not services. Fourth, your genuine advantage is understanding complex legacy systems, multi-cloud reality, and constrained operations better than most Silicon Valley founders.
Build for how enterprises actually operate. Fifth, invest in real relationships; enterprise deals run on trust and reputation.
Finally, stay focused on solving meaningful problems for serious enterprises rather than chasing valuations. That foundation builds something that endures.
As the discussion concludes, Vikrant Labde and Nikhil Ambekar emphasize Turinton AI’s commitment to ethical innovation and scalable impact.
Their shared focus on technology, strategy, and human-centric design showcases how Turinton AI is shaping the future of artificial intelligence with purpose and precision, inspiring a smarter, more connected digital ecosystem globally.
| Are you an
Entrepreneur or Startup? Do you have a Success Story to Share? SugerMint would like to share your success story. We cover entrepreneur Stories, Startup News, Women entrepreneur stories, and Startup stories
|
Read more Success stories of Indian entrepreneurs, Women Entrepreneurs & startups stories at SugerMint. Follow us on Twitter, Instagram, Facebook, LinkedIn