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Siddhant Masson, CEO and Co-Founder of Wokelo – Interview Series

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Siddhant Masson, CEO and Co-Founder of Wokelo – Interview Series

Sid Masson is the Co-Founder and CEO of Wokelo. With a background spanning strategy, product development, and data analytics at organizations like the Tata Group, Government of India, and Deloitte, Masson brings deep expertise in applying emerging technologies to real-world business challenges. At Wokelo, he is leading the company’s mission to transform how knowledge workers conduct due diligence, sector analysis, and portfolio monitoring through agentic AI frameworks.

Wokelo is a generative AI-powered investment research platform designed to automate complex research workflows, including due diligence, sector analysis, and portfolio monitoring. Using proprietary large language model (LLM)-based agents, the platform facilitates the curation, synthesis, and triangulation of data to generate structured, decision-ready outputs.

Wokelo is used by a range of organizations, including private equity firms, investment banks, consulting companies, and corporate teams, to support data-informed decision-making.

What inspired you to create Wokelo AI, and how did you identify the need for an AI-driven research assistant that could streamline due diligence, investment analysis, and corporate strategy?

Wokelo AI was born out of firsthand experience. Having spent years in management consulting at Deloitte and corporate development at Tata Group, I encountered the same challenges over and over – manual, repetitive research, data scarcity in private markets, and the sheer grunt work that slows down analysts and decision-makers.

The turning point came during my second master’s in AI at the University of Washington, where my thesis focused on Natural Language Processing. While freelancing as a consultant to pay my way through school, I built a prototype using early versions of GPT and saw firsthand how AI could turn weeks of work into days and hours – without compromising quality. That was the lightbulb moment.

Realizing this technology could revolutionize investment research, I decided to go all in. Wokelo AI isn’t just another research tool – we were some of the first people pioneering AI agents two years ago. It’s the solution I wish I had during my years in due diligence and investment analysis.

How did your experience at Deloitte, Tata, and the Government of India shape your approach to building Wokelo?

At Deloitte, as a management consultant, I worked on a variety of complex projects, dealing with research, analysis, and due diligence on a daily basis. The work was intensive, involving a lot of manual, repetitive tasks and desk research that frequently slowed down progress and increased costs. I became all too familiar with the pain points of gathering data, especially when it came to private companies, and the challenges that came with using traditional tools that weren’t built for efficiency or scalability.

Then, at Tata Group, where I worked on M&A and corporate development, I continued to face the same issues — data scarcity, slow research, and the challenge of turning raw information into actionable insights for large-scale decisions. The frustration of not having effective tools to support decision-making, particularly when dealing with private companies, further fueled my desire to find a solution.

Additionally, my work with the Government of India on the IoT solution for a water infrastructure project, further refined my understanding of how product innovation could address real-world problems on a large scale, and it gave me the confidence to apply the same approach to solving the research and analysis challenges in the consulting and investment space.

So, my professional background and my firsthand exposure to the struggles of research, analysis, and data collection in consulting and corporate development directly influenced how I approached Wokelo. I knew from experience the roadblocks that professionals face, so I focused on building a solution that not only automates grunt work but also allows users to focus on high-impact, strategic tasks, ultimately making them more productive and efficient.

Wokelo leverages GenAI for research and intelligence. What differentiates your AI approach from other summarization tools in the market?

While most competitors offer chatbot-style Q&A interfaces – essentially repackaged versions of ChatGPT with a finance-focused UI – Wokelo AI takes a completely different approach. We built an AI agent specifically designed for investment research and financial services – not just a chatbot but a full-fledged workflow automation tool.

Unlike simple summarization tools, Wokelo handles end-to-end research deliverables, performing 300-400 analyst tasks that would typically take a week. Our system autonomously identifies requirements, breaks them into subtasks, and executes everything from data extraction and synthesis to triangulation and report generation. As a result, our clients get deep, comprehensive, and highly nuanced insights – a real analysis, not just surface-level answers.

Another key differentiator is accuracy and reliability of the intel. Wokelo doesn’t make up insights, it doesn’t hallucinate – it provides fully referenced, fact-checked outputs with citations, eliminating the trust issues that many GenAI tools have. As a cherry on top, our platform users also get exportable reports in various formats typically used by analysts, making it a seamless replacement for traditional research platforms like PitchBook or Crunchbase, but with far richer intelligence on M&A activity, funding rounds, partnerships, and market trends.

Wokelo is more than just an LLM with a UI wrapper. Can you explain the deeper AI capabilities behind your platform?

Wokelo is purpose-built for investment research, combining cutting-edge AI, exclusive financial datasets, and a research-centric workflow – offering capabilities that extend far beyond a simple LLM with a UI wrapper. At its core, Wokelo leverages a Mixture of Experts (MoE) approach, integrating proprietary large language models (LLMs) pre-trained on tier-1 investment data, ensuring highly precise, domain-specific insights for investment professionals.

Designed for seamless workflow integration, Wokelo features a collaborative, notebook-style editor, allowing users to create, refine, and export well-structured, templatized outputs in PPT, PDF, and DOCX formats—streamlining research documentation and presentation. Its multi-agent orchestrator and prompt management system ensures dynamic model adaptability, while robust admin controls facilitate query log reviews and compliance rule enforcement.

By merging advanced AI capabilities with deep financial intelligence and intuitive research tools, Wokelo delivers an end-to-end investment research solution that goes far beyond a standard LLM.

How does Wokelo ensure fact-based analysis and prevent AI hallucinations when synthesizing insights?

As we serve highly reputable clients whose every decision must be backed by precise data, accuracy and credibility are at the core of our AI-driven insights. Unlike general-purpose AI platforms that may produce speculative or unverified information, Wokelo ensures fact-based analysis through a robust, citation-backed approach, eliminating AI hallucinations.

Every trend, analysis, market signal, case study, M&A activity, partnership update, or funding round insight generated by Wokelo is grounded in real, verifiable sources. Our platform does not “make up” information – each insight is accompanied by references and citations from premium data sources, trusted market intelligence platforms, tier-one news providers, and verified industry databases. Users can access these sources at any time, ensuring full transparency and confidence in the data. Wokelo has an internal fact check agent using an independent LLM to ensure every fact or data point is mentioned in the underlying source.

Additionally, Wokelo integrates with customers’ internal data repositories, unlocking valuable insights that might otherwise remain scattered or underutilized. This ensures that our AI-driven analysis is tailored, comprehensive, and aligned with specific investment-related queries.

Designed for high-stakes business decision-making, Wokelo’s AI is trained to synthesize insights, not speculate—pulling exclusively from factual datasets rather than generating assumptions. This makes Wokelo a more credible and reliable alternative to general-purpose AI tools, empowering businesses to make informed, data-driven decisions with confidence.

How does Wokelo’s AI handle real-time data aggregation across multiple sources like filings, patents, and alternative data?

Wokelo’s AI excels at real-time data aggregation by tapping into over 20 premium financial services datasets, including key sources like S&P CapIQ, Crunchbase, LinkedIn, SimilarWeb, YouTube, and many others. These datasets provide rich, reliable information that serves as the foundation for Wokelo’s analytical capabilities. In addition to these financial datasets, Wokelo integrates data from a variety of top-tier publishers, including news articles, academic journals, podcast transcripts, patents, and other alternative data sources.

By synthesizing insights from these diverse and continuously updated data streams, Wokelo ensures that users have access to the most comprehensive, real-time intelligence available. This powerful aggregation of structured and unstructured data allows Wokelo to provide a holistic view of the market, offering up-to-the-minute insights that are crucial for investment research.

Wokelo is already being used by firms like KPMG, Berkshire, EY, and Google. What has been the key to driving adoption among these high-profile clients?

Wokelo’s success among industry leaders like KPMG, Berkshire, EY, and Google stems from its ability to deliver measurable, transformative impact while seamlessly integrating with professional workflows. Unlike generic AI solutions, Wokelo is purpose-built for investment research, ensuring that its algorithms not only meet but exceed the high standards expected in this sector.

A key driver of adoption has been Wokelo’s close collaboration with leadership teams, allowing firms to embed their hard-won expertise into proprietary AI workflows. This deep customization ensures that Wokelo aligns with the nuanced decision-making processes of top investment professionals, providing best-in-class reliability and earning the trust of elite clients. These firms choose Wokelo over other tools in the market for its depth of analysis, fidelity, and accuracy.

Beyond its precision and adaptability, Wokelo delivers tangible efficiency gains. By reducing due diligence timelines from 21 to just 10 days and automating core research tasks, it significantly cuts manpower costs while freeing senior professionals from hours of manual work. With the ability to screen 5–10X more deals per month, firms using Wokelo gain a competitive edge, accelerating decision-making without compromising on depth or accuracy.

By combining cutting-edge AI, deep customization, and real-world impact, Wokelo has established itself as an indispensable tool for top-tier investment and advisory firms looking to scale their operations without missing critical details.

How does Wokelo integrate into the existing workflows of investment professionals, and what feedback have you received from users?

Wokelo integrates seamlessly into investment workflows by automating the entire deal lifecycle—from evaluating sector attractiveness to identifying high-potential companies in a global database of over 30 million firms. It offers in-depth company analysis, competitive benchmarking, and data room automation, eliminating tedious file reviews and quickly generating actionable insights. Wokelo also supports portfolio monitoring, peer analysis, and provides easy-to-export PPTs with client branding, streamlining client presentations and meeting prep.

Users report significant efficiency gains, reducing due diligence timelines from 20 days to just one week and increasing deal evaluation capacity from 100 to 500 per month—boosting deal coverage by tenfold.

How do you see AI transforming the investment research landscape in the next five years?

We’re only scratching the surface of what’s possible. AI will enable end-to-end research in a fraction of the time. With high-fidelity “super agents” capable of handling everything from deep market research and expert calls to data analysis and drafting a well-formatted 100-page deck, tasks that would traditionally require a team of five consultants working 6–8 weeks can now be accomplished much faster. This leap in speed and breadth of output will unlock new levels of productivity, allowing human experts to focus on high-level strategy and judgment.

AI will enable 50–100x more deals in the pipeline. By automating large parts of due diligence and analysis, AI-driven solutions can help investment managers expand their deal-screening capacity exponentially, uncovering more opportunities and diversifying portfolios in ways that were previously unfeasible.

The most pivotal element will be the amplified human-AI synergy. As these “super agents” take on the heavy lifting, collaboration between AI tools and human decision-makers becomes crucial. While AI will expedite processes and surface insights at scale, human expertise will remain essential for fine-tuning strategies, interpreting nuanced findings, and making confident investment decisions. This synergy will drive enhanced returns and innovation across the investment research landscape in the next five years.

As AI tools become more prevalent, how do you see human analysts and AI collaborating in the future?

As AI tools become more prevalent, the future of human analysts will revolve around collaboration rather than competition with AI. Rather than replacing analysts, AI will act as a powerful augmentation tool, automating repetitive tasks and enabling analysts to focus on higher-value, strategic work. The most successful analysts will be those who learn to integrate AI into their workflows, using it to enhance productivity, refine insights, and drive innovation. Rather than fearing AI, analysts should view it as a game-changing tool that amplifies their skills and allows them to add greater value to their organizations.

Ultimately, AI won’t replace human analysts—but analysts who embrace AI will replace those who don’t.

Thank you for the great interview, readers who wish to learn more should visit Wokelo.

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