The landscape of artificial intelligence has fundamentally shifted from experimental labs to boardroom imperatives, yet the challenge of assembling world-class AI talent remains one of the most pressing obstacles facing enterprises today. In an era where AI skill penetration in India has grown 14-fold since 2016, yet demand still outstrips supply by 51%, innovative companies are discovering that traditional hiring models cannot match the velocity requirements of modern AI transformation.
Enter the paradigm of fractional AI teams – a revolutionary approach that bridges the chasm between enterprise ambition and execution capability. Unlike conventional consulting models that deliver black-box solutions or traditional hiring cycles that can stretch for months, fractional AI teams represent a new category of embedded expertise that integrates seamlessly into existing technology stacks while maintaining the agility to deploy cutting-edge innovations at unprecedented speed.
This transformation is particularly pronounced in customer experience domains, where businesses are shifting from cost-center thinking to outcome-driven strategies powered by generative AI, Large Action Models, and advanced analytics. Organizations that once struggled to implement basic automation are now deploying sophisticated AI agents that can deliver hyper-personalized experiences, predict customer behavior, and optimize entire service ecosystems in real-time.
AI Capabilities or Risk Obsolescence
The stakes could not be higher. With India’s AI market projected to reach $28.8 billion by 2025, growing at a staggering 45% compound annual growth rate, companies face an existential choice: rapidly scale their AI capabilities or risk obsolescence in an increasingly intelligent marketplace. The traditional approach of building internal AI teams from scratch has proven inadequate – not only due to talent scarcity but also because the pace of AI evolution demands continuous adaptation that few organizations can sustain internally.
Today, we explore how pioneering companies like Superteams.ai are redefining this paradigm, creating invite-only networks of India and Asia’s top 3% AI engineers while simultaneously delivering measurable business outcomes for enterprises across the globe. This conversation examines not just the mechanics of fractional AI deployment, but the strategic imperatives driving its adoption and the transformational potential it unlocks for businesses ready to embrace the future of intelligent operations.
We are delighted to welcome Soum Paul and Debasri Rakshit, the dynamic founders of Superteams.ai, to this exclusive conversation. Soum, as Founder and CTO, leads the company’s technical vision and product innovation, driving research on next-generation AI systems and building proprietary AI workflows. Debasri, the Co-founder and COO, expertly steers operations, delivery governance, and talent development, unlocking India’s top AI engineering potential through rigorous assessment and curated upskilling.
Together, they are pioneering a fractional AI talent platform that is transforming how enterprises access top-tier AI expertise and accelerate innovation at scale.
Welcome Soum and Debasri
Q1. Soum and Debasri, let me start with a simple question that I think many of our readers are grappling with – when you founded Superteams.ai at the moment ChatGPT launched, what was the “aha moment” that made you realize the world needed a completely different approach to AI talent?
Soum:
The “aha moment” came from watching history repeat itself. When cloud computing became mainstream, enterprises rushed to migrate workloads without understanding architecture. The same pattern emerged with generative AI. Everyone wanted to “build an AI or LLM product” without knowing what they were solving for. I realised the bottleneck wasn’t infrastructure or data, it was the ability to connect existing business workflows to modern AI models, in a way that’s precise, accurate and cost-effective. To do that, we need individuals who can combine systems engineering with AI – and playbooks that allow one to do so quickly.
That’s what led to Superteams: to provide businesses a way by which they can augment their existing workflows with on-demand AI teams.
Debasri:
For me, it was seeing the disconnect between the hype and the human element. Everyone talked about AI replacing jobs, but no one talked about who was building the systems or how teams could be structured to make AI usable and responsible. We saw a need for a bridge — a platform that could bring together India’s top AI engineers, PhD students, product thinkers, and domain experts in a way that felt collaborative and human. That’s where the name “Superteams” came from: it’s about augmented intelligence and augmented teamwork.
Parallels
Q2. Debasri, coming from the publishing world at HarperCollins and Amazon-Westland to co-founding an AI platform seems like quite a journey. What parallels do you see between curating great content and curating great AI talent?
Debasri:
It’s surprisingly similar. In publishing, you’re curating voices — finding writers who can communicate complex ideas with clarity, and matching them with the right editors, designers, and marketers. At Superteams, we’re curating problem-solvers — engineers and researchers who are the best fit for businesses, who can translate complex problems into systems that work. The process of editorial curation and AI talent curation both require systems thinking, product management, and empathy. You’re essentially saying: “Here’s a great idea. Let’s assemble the right minds to bring it to life.”
Q3. Soum, you’ve been part of multiple successful startups and have two published books. How does building Superteams.ai differ from your previous ventures, especially given the unique challenges of the AI landscape?
Soum:
The startups I was part of before Superteams.ai had raised large venture rounds and focused on scaling a single product.
With AI, however, the landscape evolves at an unprecedented pace — models, frameworks, and architectures change almost monthly. To thrive, you need to stay nimble, experiment constantly, and adapt fast. At Superteams.ai, we’ve built that philosophy into our DNA by taking an R&D-first approach. We groom teams that can orchestrate human and machine intelligence together to solve dynamic, complex business problems.
The other major difference is strategic: we’ve chosen a revenue-first path instead of relying on large venture rounds. This forces discipline — we build solutions that pay for themselves, deliver immediate business impact, and evolve in tandem with actual business needs.
Exceptional AI Talent
Q4. When you say “top 3% of engineers,” what does that actually look like in practice? Walk us through what separates exceptional AI talent from good AI talent.
Soum:
There’s a significant gap in the market right now. Most universities, colleges, and workshops teach students how to trainAI models — but only a handful of companies in the world have the resources to actually do that at scale.
The real opportunities lie in building systems around AI models — engineers who understand retrieval pipelines, vector databases, human feedback loops, and what it takes to make models production-ready. That’s where exceptional AI talent stands out.
This requires a unique combination of technical knowledge, agility, and design-thinking skills.
Debasri:
Additionally, communication skills, a bit of critical thinking, and attention to detail are increasingly important in the age of AI. We look for individuals who can understand the business problem, collaborate effectively with customers, and translate solutions clearly. Most importantly, they need the attention to detail to anticipate and prevent failures, and ensure the AI system performs reliably in every scenario.
Business
Q5. Let’s talk about the customer side – what are you seeing in terms of how enterprises are approaching their AI transformation initiatives? Are they coming to you with clear roadmaps or are they still figuring it out?
Debasri:
Most are still in the “figuring it out” stage. The excitement is there, but it takes some time to hone in on the exact solution that will alter existing processes.
We often start with exploratory workshops — helping clients define what success looks like, whether that’s automating document workflows, improving support systems, analyzing call recordings, launching AI-powered chatbots or building internal AI assistants/copilots.
Most importantly, we explain data privacy and sovereignty and help enterprises understand why they should leverage open-source AI.
In fact, we recently launched an AI Discovery workshop series, where we work through the various problem statements in an organization and explore how AI can help solve those problems. This helps them see the return-on-investment from AI before building a full in-house AI team. Once the right structure and roadmap are in place, the implementation process becomes smoother and more effective.
The Fractional Model
Q6. The fractional model is interesting because it’s not traditional consulting, but it’s also not full-time hiring. How do you position this offering when prospects are used to binary choices?
Debasri:
We position it as “on-demand capability.” Companies don’t always need full-time AI teams; they need access to the right expertise at the right stage.
Fractional teams allow them to experiment, scale, or pivot without the long hiring cycle or cost of traditional consulting. It’s like having a strike force that can prototype and deploy within 30-60 days.
Q7. Debasri, with your background in content and operations, how do you ensure quality control when you have distributed teams working on different client projects simultaneously?
Debasri:
Quality is cultural. We built an internal playbook called the Superteams Delivery Framework — it outlines not just coding standards but communication protocols, documentation norms, and review cycles.
Every project goes through a dual lens: technical validation by our senior engineers and outcome validation by project leads. My publishing background probably helps here — I think of every client deliverable as an “edition” that has to meet editorial standards before it goes out.
Strategic Implications
Q8. Looking at the broader market dynamics, India has this massive AI talent pool but also a 51% demand-supply gap. How is Superteams.ai positioning itself within this paradox, and what does this mean for global AI development?
Soum:
India’s paradox is its power. We have volume, but uneven depth. Our mission is to unlock that depth — by identifying and elevating the top 3% of AI builders who can work on global-grade systems. We’re already seeing our network supporting U.S., U.K., and Middle Eastern companies that want both cost efficiency and high technical rigor. In a sense, Superteams is creating the operating system for India’s AI exports.
Debasri:
It also changes the global narrative. Instead of “outsourcing,” we talk about co-building. These are Indian engineers designing global AI-powered products for both Indian and global enterprises. That’s a powerful shift — from a service economy to a product R&D ecosystem.
Q9. Soum, your platform emphasizes “sovereign AI” – companies owning their data, workflows, and models. Given the current geopolitical climate and data localization requirements, how critical is this positioning for enterprise adoption?
Soum:
It’s actually at the heart of what we do. The next phase of AI isn’t just about which model you pick — it’s about who owns the stack. The companies we work with want full control over their data, embeddings, model weights, and agents.
Sovereign AI is really about autonomy. It means taking open models, fine-tuning them privately, and running them in secure, compliant environments. That control builds trust, keeps you on the right side of regulations, and creates real long-term value. It also is more cost-effective in the long run.
In today’s world, if you don’t own your AI stack, you’re leaving too much to chance.
System Thinking
Q10. The concept of “System Thinking” versus one-size-fits-all frameworks is fascinating. Can you elaborate on how this philosophy translates into actual client outcomes? What does a bespoke AI system integration look like in practice?
Soum:
System Thinking is about seeing every AI problem as part of a larger workflow, infrastructure, and product ecosystem— not just a standalone model or tool.
Take a customer support bot, for instance. It’s not simply a wrapper over an OpenAI model. A truly bespoke system integrates structured and unstructured data, data enrichment, ETL/ELT pipelines, embedding generation, reranking, and MLOps. Instead of dropping in a prebuilt chatbot, we design the entire pipeline: context retrieval, ranking, orchestration, and human-in-the-loop review.
This leads to a system that learns and improves over time, rather than degrading, and it’s fully aligned with the client’s unique business processes. That’s the tangible outcome of System Thinking — AI that fits seamlessly into how a company actually works.
Q11. You mention enabling businesses to build “production-ready AI workflows” rather than just experimentation. What’s the maturity gap you’re seeing between AI pilots and actual production deployment, and how do fractional teams bridge this?
Soum:
The gap between AI pilots and production-ready systems is massive. Many pilots prove a model can work in a controlled environment, but scaling it reliably in production is a different challenge altogether. It’s not just about accuracy — it’s about infrastructure, reliability, monitoring, and maintainability.
Most organizations underestimate the complexity of MLOps, DevOps, and orchestration pipelines. Production systems need automated data ingestion, monitoring, retraining pipelines, versioned models, robust error handling, observability, and seamless integration with existing business workflows. Without these, pilots often degrade or fail under real-world conditions.
Our teams design and deploy production-grade architectures, implement CI/CD for AI, set up monitoring and alerting, and ensure workflows are resilient and scalable. Essentially, they translate experimental AI into reliable, operational systems that deliver consistent business impact.
Operational Perspective
Q12. From an operational perspective, how do you balance the need for rapid deployment – you mention 30-day timelines – with the complexity of enterprise-grade AI systems that typically require extensive customization?
Soum:
Speed and structure can absolutely coexist. Our 30-day framework isn’t about cutting corners — it’s about modular building. We rely on pre-validated components like retrieval pipelines, model adapters, and security wrappers. Think of it like a LEGO approach: the blocks are battle-tested, so we can assemble solutions quickly, while customization happens at the orchestration layer, not by reinventing the wheel.
Within 30 days, our goal is to give businesses clear visibility into how AI would work within their workflows. If they choose to fully productize it, it may take a bit longer — that phase involves setting up monitoring, reliability, MLOps, and infrastructure that turn an MVP into a fully functioning, production-grade system.
Future-Forward
Q13. Looking at the evolution of AI roles – we’re seeing emergence of prompt engineers, LLM ops specialists, AI governance roles – how is this shifting the skill composition of your network, and what does this mean for traditional software engineering?
Soum:
The rise of AI-specific roles is reshaping the skill landscape significantly. In our network, we’re seeing a blend of traditional software engineering with AI-native skills. Prompt engineers, LLM ops specialists, and AI governance experts are no longer niche roles — they’re becoming essential to building robust, scalable, and compliant AI systems.
For traditional software engineers, this shift means the baseline expectations are evolving. It’s no longer enough to write clean code; engineers now need to understand model behavior, data pipelines, human-in-the-loop workflows, and orchestration across AI and existing systems.
In practice, this leads to hybrid teams where engineers combine coding expertise with AI literacy, allowing organizations to move from experimentation to production-ready AI faster and more reliably. It’s a transformation from purely software-centric thinking to system-centric, AI-aware engineering.
Highest ROI for Enterprises
Q14. The case study mentioning 40% faster contract vetting and 35% better compliance is compelling. As you scale these types of implementations, what patterns are you seeing in terms of which use cases deliver the highest ROI for enterprises?
Debasri:
The highest ROI almost always comes from taming document-heavy workflows. Technically, this is unstructured data, which makes up 80–90% of the information enterprises deal with — think documents, annual reports, call recordings, scanned invoices, email threads, contracts, video files, and more.
Many enterprises still rely on teams to manually process this information, which is slow, repetitive, and error-prone. The systems we build automate these workflows while remaining fully verifiable by humans, striking a balance between speed and trust.
Q15. Debasri, with your climate change certification and focus on sustainable innovation, how do you see environmental considerations influencing AI deployment decisions, particularly for ESG-conscious enterprises?
Debasri:
It’s becoming a defining factor. ESG-conscious companies are asking: “What’s the carbon footprint of my AI system?” We encourage clients to adopt green AI practices — optimizing model sizes, using GPU-efficient cloud infrastructure, and measuring energy use.
Also, AI can help build climate-tech products like ESG reporting systems, Scope 1, 2, 3 measurement systems, and accessible training manuals for employees. Personally, I see sustainability and efficiency as the same design principle: use only what you need, and make it last longer.
2025 and Beyond
Q16. As we look toward 2025 and beyond, where do you see the biggest opportunities for fractional AI teams? Which industries or use cases are still underexplored but represent significant potential?
Soum:
Manufacturing, logistics, and healthcare. These sectors have rich data but fragmented systems. Fractional teams can come in, stitch the data layers, and deploy automation with measurable ROI.
We are also planning to launch a platform that streamlines sovereign AI deployment for enterprises, which will enable enterprise teams to leverage a suite of modern AI workflows without having to worry about deployment and orchestration.
Debasri:
And I’d add education and climate-tech. There’s so much room to apply generative AI to learning and environmental data interpretation. The goal should be not just smarter businesses, but smarter citizens and more resilient ecosystems.
Strategic Vision
Q17. If you had to advise a CTO or Chief Innovation Officer who’s been tasked with “implementing AI” but is overwhelmed by the complexity and options, what would be your framework for thinking about this challenge?
Soum:
Start with a map, not a model. Identify where data flows, where decisions are made, and where inefficiencies lie. Then choose one high-impact use case that connects these layers. Don’t chase novelty — chase compounding value. And bring in a small fractional team early; in the AI domain, velocity matters.
Debasri:
I’d say: make it human. Define your north star: what does success mean for your users or teams? Once that’s clear, AI becomes a tool, not a distraction. The best implementations we’ve seen are the ones that improve how people think and collaborate, not just how they automate.
Q18. Finally, as founders who are literally building the infrastructure for India’s AI talent to serve global markets, what’s your vision for how this model could reshape not just individual companies, but entire industry ecosystems?
Soum:
We see Superteams as a bridge between enterprises and the next generation of AI talent in India — enabling companies to transition into an AI-first era while nurturing world-class capability within the Indian talent ecosystem. Our goal is to make AI adoption not just easier, but deeply local — where global innovation is powered by India’s technical excellence.
Debasri:
We see India as equal to Silicon Valley or China in terms of AI talent. There’s drive, there’s talent, and there’s boundless ambition. Our vision is to build a future where Indian engineers build products which are on par with global standards.
Closing
The conversation with Soum Paul and Debasri Rakshit reveals a fundamental truth about the current state of enterprise AI transformation: the traditional paradigms of talent acquisition, technology deployment, and business model innovation are being rapidly obsoleted by more agile, intelligent approaches to accessing and implementing artificial intelligence capabilities.
Their journey with Superteams.ai illuminates several critical insights that extend far beyond the boundaries of their specific platform. First, the recognition that AI transformation is not merely about adopting new tools, but about fundamentally reimagining how organizations access, deploy, and scale technical expertise in an era of unprecedented technological velocity. The fractional model they’ve pioneered addresses a market failure that extends across industries – the inability of traditional hiring and consulting models to match the pace and specificity requirements of modern AI implementation.
Sovereign AI
The emphasis on sovereign AI principles resonates particularly strongly in our current geopolitical climate, where data ownership, algorithmic transparency, and strategic technological independence have become not just operational considerations but competitive imperatives. Superteams.ai’s approach of ensuring that enterprises maintain complete ownership of their data, workflows, and models while still accessing world-class implementation expertise represents a sophisticated understanding of the tensions between innovation velocity and strategic control.
Perhaps most significantly, their success in creating measurable business outcomes – demonstrated through case studies showing 40% faster processing times, 35% improved compliance, and 30% cost reductions – validates the core thesis that AI transformation, when executed thoughtfully, delivers not just operational efficiency but fundamental business model advantages. These aren’t marginal improvements but transformational shifts that can redefine competitive positioning within entire industry segments.
The broader implications for customer experience transformation are equally profound. As businesses migrate from cost-center thinking to outcome-driven strategies powered by AI, the organizations that succeed will be those capable of rapidly deploying, iterating, and scaling AI-powered customer touchpoints that deliver hyper-personalized, predictive, and continuously optimized experiences. The fractional AI team model provides the technical foundation for this transformation while maintaining the flexibility to adapt as customer expectations and technological capabilities continue to evolve.
Looking Forward
Looking forward, the Superteams.ai model suggests a future where the most successful enterprises will be those that master the art of intelligent resource orchestration – knowing precisely when to build internally, when to partner, and when to leverage fractional expertise to accelerate innovation cycles. This represents a maturation of business strategy thinking that acknowledges the reality that sustained competitive advantage in AI-driven markets comes not from hoarding talent or technology, but from optimizing access to and deployment of the most appropriate expertise for each specific challenge.
For customer experience leaders, technology executives, and business strategists, the key takeaway extends beyond the specific mechanics of fractional AI teams to a broader strategic imperative: the organizations that will dominate the next decade of business competition will be those that develop superior capabilities for rapidly identifying, accessing, and deploying the specific technical expertise required to transform customer experiences through artificial intelligence. The companies that continue to rely on traditional talent acquisition and technology deployment models risk not just competitive disadvantage, but strategic obsolescence in an increasingly intelligent marketplace.
The conversation with Soum and Debasri ultimately points toward a future where the boundaries between internal capabilities and external expertise become increasingly fluid, where the speed of AI implementation becomes a primary competitive differentiator, and where the ability to maintain strategic control while accessing world-class technical capabilities determines which organizations will lead the next wave of customer experience innovation.
