Google’s Data Center Hub in AP to bolster India’s AI ecosystem: Turinton AI Co-Founder

Co-Founder & CTO of Mumbai-based Turinton AI, Vikrant Labde's entrepreneurial journey began with Cuelogic Technologies, which he co-founded and scaled into a 400-strong organization before its acquisition by LTIMindtree. At Turinton, Vikrant combines his passion for AI, GenAI, RAG, and data engineering with his ability to mentor high-performing teams of engineers and designers.

In an exclusive interview with the Bizz Buzz, Vikrant said: "We positioned ourselves entirely differently from day one. We didn't build another data platform or another ML toolkit. We built reasoning engines that understand business context, how operations actually work, what constraints matter, and how decisions get made.

Our zero data movement architecture operates directly on existing enterprise infrastructure rather than forcing massive data lakes and infrastructure overhauls. We have deployed 110 plus use cases across industry verticals within our very first year of operation, and we are just getting started. That's India's first platform making existing systems intelligent without reinvention."

Google recently announced a $15 billion investment in an AI powered data center hub in Visakhapatnam, Andhra Pradesh. How do you see such investments transforming India's industrial and AI landscape?

This investment changes India's infrastructure story fundamentally. India has been constrained by cloud availability and compute capacity for AI workloads. Google's commitment through the Visakhapatnam center creates local infrastructure advantages through lower latency, better data residency compliance, and significantly reduced bandwidth costs.

That matters deeply for industries distributed geographically across India. For the AI ecosystem, this enables platforms like Turinton to scale faster without enterprises worrying about data sovereignty or cross border compliance complications.

The industrial impact is transformative. Industries in tier 2 and tier 3 cities can now access enterprise grade AI infrastructure locally, democratizing adoption beyond just large multinationals in metros. This investment will accelerate Indian manufacturing & digital transformation by 3 to 5 years.

It signals that India is serious about becoming an AI hub for real enterprise intelligence, not just talent outsourcing. That attracts global investment and talent, strengthening the entire ecosystem substantially.

What advice would you give to enterprises looking to scale AI responsibly and effectively in 2025 and beyond?

Start with absolute clarity on what problem you are solving and who owns success organizationally. AI amplifies operational capability but won't fix broken processes or poor fundamentals. Build AI on top of strong operational foundations.

Second, resist centralization pressure. Your data architecture mirrors your operational architecture. If data is distributed across systems, that's how your business actually operates. Build AI solutions working within that reality rather than forcing infrastructure changes.

Third, prioritize explainability from day one because you cannot govern what you cannot understand. Fourth, measure success through business outcomes, not technical metrics.

There is a growing feeling among many that AI throws many out of their jobs. Is it true or will it lead to generation of new job opportunities for many. Please elaborate.

This deserves an honest, nuanced answer. AI will absolutely displace certain work including repetitive manual tasks, basic data processing, and simple analysis jobs.

That's real and we shouldn't minimize it. But that's only half the story. In every industrial transition spanning mechanization, electrification, and digitization, initial job displacement was real but overall job creation exceeded displacement because new capabilities created new problems to solve.

Manufacturing companies we work with don’t reduce headcount deploying AI for predictive maintenance. They retrain teams to work with intelligent systems and create roles for people interpreting AI recommendations, governing decisions, and making business strategy on top.

In BFSI, fraud detection AI redeploys analysts from catching known fraud to investigating new patterns and setting policy. Enterprises scaling AI most effectively treat it as augmentation technology, not replacement. They invest in reskilling and creating new governance roles around AI.

The displacement risk concentrates in roles doing pure data processing. The opportunity expands in roles combining human expertise with AI augmented insights. India's advantage is treating technology as human capability amplification within existing business structures.

How does Turinton ensure AI adoption is aligned with business outcomes rather than just technology deployment?

Alignment happens through both architecture and rigorous process discipline. On architecture, we have embedded business context into reasoning engines from the foundation. Our systems optimize for business KPIs like margin improvement, cycle time reduction, and quality metrics, not model accuracy.

We build governance frameworks making business trade-offs explicit and transparent. In the process, we start every engagement by defining business outcomes first, with technology following.

We identify the business problem owner, the operations leader whose P&L depends on success. That person drives requirements and validates impact continuously.

We measure success through business metrics at 8 to 12 week intervals, not at project completion. If business outcomes are not materializing, we pivot immediately rather than pushing forward with wrong approaches.

We embed change management throughout, helping teams understand AI usage, updating decision processes, and building confidence through early wins. Technology deployment without business alignment is just IT spending.

What are the key differentiators of Turinton compared to other enterprise AI solutions in the market?

Most enterprise AI platforms share a fundamental limitation: they require data extraction, centralization, and massive movement. That creates dependency, increases cost, and delays deployments. Our architectural differentiation is complete.

First, our zero data movement approach means intelligence operates where data lives, eliminating the entire ETL tax and infrastructure burden. Second, we’ve embedded business aware reasoning into the platform foundation.

Our systems understand operational constraints, compliance requirements, and business rules inherently, not as configuration layers. Third, explainability is built by design.

Every recommendation includes the reasoning trail enterprises need for governance and trust. Fourth, we deliver production outcomes in 8 to 12 weeks versus 18 months industry standard.

Fifth, our industry specific reasoning engines embed domain knowledge that generic platforms simply cannot replicate. We compete on fundamentally different architecture designed for how enterprises actually operate.

How has Turinton helped enterprises overcome typical challenges in AI adoption, such as fragmented data and long implementation cycles?

Fragmented data and implementation delays are architectural problems, not data problems. Traditional platforms view siloed data as a challenge requiring centralization and extraction. We treat it as operational reality and build our architecture directly around it.

Our federated reasoning engine operates across decentralized data sources including ERP systems, Accounting and supply chain systems, customer success and other important IT systems without moving anything. We reason about relationships between data across your systems without extraction or consolidation.

On implementation cycles, we have restructured deployment entirely. Instead of 18 month projects requiring massive upfront infrastructure investment and operational pauses, we work in 8 to 12 week sprints delivering measurable business outcomes at each stage.

We integrate with existing infrastructure, whether cloud or on premise, so enterprises maintain operations throughout. This approach has helped 110 plus use cases reach production where fragmentation became an advantage once properly designed.

Could you share an example of a successful AI deployment that delivered measurable business impact for one of your clients?

We deployed an AI system for a leading airline catering company optimizing supply chain operations across multiple vendors and sources. The challenge was real time visibility into ingredient sourcing, vendor performance, and demand forecasting across fragmented systems.

Using our zero data movement architecture, we built reasoning directly across their ERP, inventory systems, and vendor management platforms without any extraction. Within 12 weeks, the system delivered daily optimization recommendations.

Business impact was substantial: 15 percent reduction in inventory carrying costs, 22 percent improvement in vendor performance visibility, and 8 percent faster order fulfillment cycles. Operations teams understood recommendations because we built explainability throughout the decision logic.

This exemplifies what business aware AI delivers: not impressive technical metrics but measurable operational and financial outcomes. The client now uses this system daily across their entire supply chain network.

In your view, how will India's AI ecosystem evolve in the next 3–5 years, especially in sectors like BFSI, manufacturing, and retail?

India's AI evolution will be driven by enterprise pragmatism, not research papers or technological trends. In manufacturing, rapid adoption of real time optimization for predictive maintenance, quality assurance, and supply chain visibility will accelerate because the industry has sophisticated infrastructure already.

They simply need intelligent layers on top. BFSI will see AI deeply embedded in operations including fraud detection, credit decisioning, and customer behavior analysis, but constrained by regulation and compliance that global platforms don't understand. Indian companies have an advantage here because we have built governance into foundations.

Retail will transform through personalization and inventory optimization, though inventory management remains the bottleneck across Indian retail operations.

The ecosystem trend I see is enterprises prioritizing rapid deployment and measurable ROI over impressive algorithms.

That's where Indian companies will win because we understand operational reality better than Silicon Valley competitors.