Sameet Gupte, CEO and Co-Founder of EvoluteIQ – Interview Series

Sameet Gupte

, CEO and Co-Founder of EvoluteIQ, is a London-based executive with extensive experience building and scaling global technology businesses. Prior to founding EvoluteIQ, he served as Group CEO of Servion, a Cisco portfolio company, along with its affiliated businesses Innoveo AG and Acqueon Inc., where he helped drive enterprise transformation and growth. Earlier in his career, he was Executive Vice President and Managingector for Global Financial Services and Europe at Virtusa, where he led significant organic expansion, developed industry-focused solutions, and played a key in strategic acquisitions across Europe, including in Germany and Sweden, as well as the Polaris acquisition. He also previously led Genpact’s capital markets business in Europe, where he was responsible for accelerating regional growth and strengthening its position in financial services.

EvoluteIQ



is an AI-native enterprise automation company focused on transforming how businesses operate through its flagship EIQ platform. Designed to automate end-to-end processes rather than isolated tasks, the platform combines artificial intelligence, low-code tools, and process orchestration to enable systems that can adapt, make decisions, and continuously improve over time. By integrating data, workflows, and AI into a unified architecture, EvoluteIQ helps organizations streamline operations, reduce complexity, and build resilient, self-optimizing systems across industries such as finance, healthcare, and telecommunications.

Before founding EvoluteIQ in 2019, you held senior leaderships at Servion, Virtusa, and Genpact where you worked extensively on enterprise transformation and large-scale technology deployments. What experiences during that time led you to conclude that the next generation of automation platforms needed to be built as AI-native systems rather than extensions of traditional workflow or RPA tools?

Across industries, successful organizations are distinguished by their ability to consistently deliver world-class outcomes. A common factor among them is a robust process. While conceptualizing EvoluteIQ, we aimed to develop a technology that underpins this process. Our focus was on addressing the problem rather than individual components of it. Prior to 2019, available tools and technologies primarily solved for specific aspects of the process such as data extraction task management and workflow. Each of these technologies addressed a particular issue within the process but lacked a comprehensive solution for the end-to-end process. This identified a significant opportunity to create a technology that is autonomous, capable of self-learning and adapting to optimize the process. Thus, the concept of EIQ was born: a native AI platform designed to be autonomous, solve the problem rather than individual components and maximize automation. It would be low code/no code, equipped with the necessary capabilities for any process, including streaming data, events and orchestration with AI as the core framework.

Many automation platforms today are adding generative AI capabilities onto legacy infrastructure. EvoluteIQ was designed from the ground up with intelligence and autonomy at its core. What architectural choices did you make early on that allow your platform to support agentic automation in ways older systems cannot?

The platform was designed from the ground up to enable end-to-end automation of processes regardless of the type of automation required (robotic, workflow, data, events etc.).  This was done with the expectation that, over time, new techniques of automation will be invented, and existing ones may become obsolete. So, the underlying microservice-based architecture allowed for the development and inclusion of Generative AI and agentic automation.  In the same way this architecture will allow for the inclusion of Large Action Models and Quantum Decision Making soon.

EvoluteIQ is built around what you call Agentic Mesh Architecture, or {aMa}, which enables networks of intelligent agents to collaborate across enterprise processes. How does this approach differ from traditional automation frameworks, and why do you believe agent-to-agent collaboration will become a foundational layer in enterprise software?

The {aMa} is the proprietary architecture that is one of the core differentiators of the EvoluteIQ platform. Agent-to-agent collaboration enables multiple agents (both EIQ and third-party) to self-organize as they work towards the completion of a specific business outcome. This will allow customers to re-evaluate their perception and use of automation. They will no longer need to define the steps required to execute an end-to-end process; instead, they will simply need to articulate the desired business outcome and allow the agents to determine how this should be achieved. This self-organizing and self-governing capability will enable customers to create agile and resilient business operating models that automatically adapt to innovation and disruption without the need for costly and risky human intervention.

Your platform focuses on end-to-end automation of complex business processes rather than simply automating individual tasks. How does an agentic model change the way organizations think about process orchestration, decision making, and operational autonomy?

We partially covered this above, but I would add that the EIQ platform enables customers to create an abstraction layer above their existing infrastructure. Consequently, all elements of the infrastructure can be considered components of an EIQ process. In conjunction with the Agentic Mesh Architecture {aMa}, IT teams can now create ‘composable IT’ allowing businesses to develop applications and processes on demand from pre-defined, tested and approved components. This introduces a greater level of operational autonomy for business users enabling them to create, use and retire IT services as required, removing the traditional dependency on IT.

EvoluteIQ uses an outcomes-based pricing model rather than charging per bot or per user. What motivated that decision, and how does it change the way enterprise leaders evaluate the return on investment of automation initiatives?

The decision to structure outcomes-based pricing was made to align with the business’ or use case’s success criteria, ensure cost predictability, transparency and share the risk of success or failure with the customer. The most significant change for enterprises in an outcomes-based model is that unlike the traditional model where return on investment (ROI) is debated after the fact, in this case it is contracted upfront, measured continuously and paid against delivery.

One of the biggest challenges in enterprise AI is integrating structured data, unstructured information, and real-time operational signals across multiple systems. How does EvoluteIQ address these integration challenges while maintaining governance and reliability?

The EIQ platform manages structured data, unstructured data and real-time events through separate engines. Each engine comprises a set of microservices that can be scaled vertically and horizontally and replicated to provide resilience. This architecture allows the platform to be built and scaled to meet the performance, capacity and resilience requirements of individual customers.

As enterprises begin to deploy autonomous AI agents that can make decisions and trigger actions, concerns around oversight and accountability become more important. What governance frameworks or safeguards do you believe are necessary for agentic systems operating at scale?

The platform includes standard features such as auditing of process steps, recording the value of process variables and governance of Generative AI and agents. These features enable real-time governance of agentic behavior and historical reporting and analytics of their work and decisions. Beyond these features, the EIQ platform provides a unique capability that ensures agents behave as expected. By connecting to each data source and system of record within a customer, agents understand the business context they are operating in. Consequently, their responses are created within this comprehensive framework ensuring the accuracy and reliability of their actions.

EvoluteIQ reportedly achieved profitability within its second year of operation, which is uncommon for many AI startups. What strategic decisions allowed you to balance innovation and rapid product development with financial sustainability?

EvoluteIQ has been EBITDA positive since its second year of operation. This has been achieved by being prudent with costs without compromising innovation. The company focuses on customer adoption and delight, reflected in a consistently tracked 120% Net Retention Rate. It avoids overinvesting in marketing and sales by building strong partnerships with Global System Integrators Tier 1 BPO companies and leading management consulting firms to drive the Go-To-Market (GTM) motion. The company demonstrates value to prospects through paid Proof Of Value (POV) rather than free Proof of Concepts (POC). Every growth decision has been deliberate and focused. Enabling strong partnerships and making clients self-sufficient on the EIQ platform has also resulted in a lean support and implementation organization, ensuring a healthy employee-to-revenue ratio in line with best-in-class metrics.

The enterprise automation landscape is evolving quickly, with traditional RPA vendors, generative AI platforms, and agentic automation companies all competing for attention. How do you see the competitive dynamics of this market evolving over the next few years?

In enterprise automation, we are witnessing a fundamental convergence rather than merely increased competition. Traditional RPA is being relegated to an execution layer, generative AI is rapidly becoming ubiquitous and agentic AI is emerging as the control paradigm. However, none of these developments in isolation will define the market. The real battleground is shifting towards AI-native orchestration platforms that can manage end-to-end processes and deliver measurable business outcomes. Over the next few years, the winners will not be those with the most advanced models or the largest number of bots but those that can combine agent workflows data and decisions into a unified system that is governed auditable and continuously improving. Enterprise buyers are already moving away from tools and licenses towards outcomes and accountability. Therefore, the defining question for any platform will be whether it can take ownership of the outcome not just a part of the process.

Looking ahead, what milestones will signal that enterprises are moving from AI-assisted workflows to truly autonomous operations, and what do you expect EvoluteIQ to play in that transition?

The transition from AI-assisted workflows to true autonomous operations is not about minor efficiency gains but a fundamental rethinking of how work is conducted. The initial milestone is human-on-the-loop: AI agents make most decisions with human intervention reserved for exceptions. Subsequently, closed-loop processes will enable real-time sensing, decision-making, action and self-correction without manual intervention. Enterprises will then adopt outcomes-based models where success is measured by business results rather than activity. Ultimately, true autonomy will be realized when composable agentic architectures orchestrate across functions such as finance operations and customer service without being constrained by legacy systems.

EvoluteIQ is at the forefront of this transition. Built as a unified AI-native platform encompassing workflow data events and decisioning it not only assists humans but also executes end-to-end processes, monitors every action, continuously measures outcomes and self-optimizes. EvoluteIQ enables AI within processes and provides the infrastructure for enterprises to operate autonomously at scale with complete accountability. This is how organizations move from automation to autonomy and why EvoluteIQ is uniquely positioned to lead this journey.

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EvoluteIQ



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