Sid Masson is the Co-Founder and CEO of Wokelo. With a background spanning strategy, product development, and data analytics at organizations just 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’s leading the corporate’s mission to remodel how knowledge employees conduct due diligence, sector evaluation, 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 evaluation, and portfolio monitoring. Using proprietary large language model (LLM)-based agents, the platform facilitates the curation, synthesis, and triangulation of information to generate structured, decision-ready outputs.
Wokelo is utilized by a spread of organizations, including private equity firms, investment banks, consulting corporations, and company teams, to support data-informed decision-making.
What inspired you to create Wokelo AI, and the way did you discover the necessity for an AI-driven research assistant that might streamline due diligence, investment evaluation, and company strategy?
Wokelo AI was born out of firsthand experience. Having spent years in management consulting at Deloitte and company development at Tata Group, I encountered the identical challenges again and again – manual, repetitive research, data scarcity in private markets, and the sheer grunt work that slows down analysts and decision-makers.
The turning point got here during my second master’s in AI on 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 labor into days and hours – without compromising quality. That was the lightbulb moment.
Realizing this technology could revolutionize investment research, I made a decision to go all in. Wokelo AI isn’t just one other research tool – we were a few of the first people pioneering AI agents two years ago. It’s the answer I wish I had during my years in due diligence and investment evaluation.
How did your experience at Deloitte, Tata, and the Government of India shape your approach to constructing Wokelo?
At Deloitte, as a management consultant, I worked on quite a lot of complex projects, coping with research, evaluation, and due diligence every day. The work was intensive, involving plenty of manual, repetitive tasks and desk research that steadily slowed down progress and increased costs. I became all too conversant in the pain points of gathering data, especially when it got here to non-public corporations, and the challenges that got here with using traditional tools that weren’t built for efficiency or scalability.
Then, at Tata Group, where I worked on M&A and company development, I continued to face the identical 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 coping with private corporations, further fueled my desire to seek out an answer.
Moreover, 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 big scale, and it gave me the arrogance to use the identical approach to solving the research and evaluation challenges within the consulting and investment space.
So, my skilled background and my firsthand exposure to the struggles of research, evaluation, and data collection in consulting and company development directly influenced how I approached Wokelo. I knew from experience the roadblocks that professionals face, so I focused on constructing an answer that not only automates grunt work but in addition allows users to concentrate 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 out there?
While most competitors offer chatbot-style Q&A interfaces – essentially repackaged versions of ChatGPT with a finance-focused UI – Wokelo AI takes a very different approach. We built an AI agent specifically designed for investment research and financial services – not only a chatbot but a full-fledged workflow automation tool.
Unlike easy summarization tools, Wokelo handles end-to-end research deliverables, performing 300-400 analyst tasks that will typically take every week. Our system autonomously identifies requirements, breaks them into subtasks, and executes the whole lot from data extraction and synthesis to triangulation and report generation. Because of this, our clients get deep, comprehensive, and highly nuanced insights – an actual evaluation, not only surface-level answers.
One other 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 utilized by analysts, making it a seamless substitute 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 greater than just an LLM with a UI wrapper. Are you able to 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 reach far beyond an easy 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 incorporates 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 normal LLM.
How does Wokelo ensure fact-based evaluation and forestall AI hallucinations when synthesizing insights?
As we serve highly reputable clients whose every decision have to be backed by precise data, accuracy and credibility are on the core of our AI-driven insights. Unlike general-purpose AI platforms which will produce speculative or unverified information, Wokelo ensures fact-based evaluation through a sturdy, citation-backed approach, eliminating AI hallucinations.
Every trend, evaluation, market signal, case study, M&A activity, partnership update, or funding round insight generated by Wokelo is grounded in real, verifiable sources. Our platform doesn’t “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 info. Wokelo has an internal fact check agent using an independent LLM to make sure every fact or data point is mentioned within the underlying source.
Moreover, Wokelo integrates with customers’ internal data repositories, unlocking precious insights that may otherwise remain scattered or underutilized. This ensures that our AI-driven evaluation 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 reasonably 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 lots of others. These datasets provide wealthy, reliable information that serves as the muse for Wokelo’s analytical capabilities. Along with these financial datasets, Wokelo integrates data from quite a lot of top-tier publishers, including news articles, academic journals, podcast transcripts, patents, and other alternative data sources.
By synthesizing insights from these diverse and constantly updated data streams, Wokelo ensures that users have access to probably the most comprehensive, real-time intelligence available. This powerful aggregation of structured and unstructured data allows Wokelo to offer a holistic view of the market, offering up-to-the-minute insights which might be crucial for investment research.
Wokelo is already getting used by firms like KPMG, Berkshire, EY, and Google. What has been the important thing to driving adoption amongst these high-profile clients?
Wokelo’s success amongst industry leaders like KPMG, Berkshire, EY, and Google stems from its ability to deliver measurable, transformative impact while seamlessly integrating with skilled 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 on 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 select Wokelo over other tools out there for its depth of research, fidelity, and accuracy.
Beyond its precision and adaptableness, Wokelo delivers tangible efficiency gains. By reducing due diligence timelines from 21 to only 10 days and automating core research tasks, it significantly cuts manpower costs while freeing senior professionals from hours of manual work. With the flexibility to screen 5–10X more deals per thirty days, 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 trying to scale their operations without missing critical details.
How does Wokelo integrate into the present workflows of investment professionals, and what feedback have you ever received from users?
Wokelo integrates seamlessly into investment workflows by automating the whole deal lifecycle—from evaluating sector attractiveness to identifying high-potential corporations in a worldwide database of over 30 million firms. It offers in-depth company evaluation, competitive benchmarking, and data room automation, eliminating tedious file reviews and quickly generating actionable insights. Wokelo also supports portfolio monitoring, peer evaluation, 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 only one week and increasing deal evaluation capability from 100 to 500 per thirty days—boosting deal coverage by tenfold.
How do you see AI transforming the investment research landscape in the subsequent 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” able to handling the whole lot from deep market research and expert calls to data evaluation and drafting a well-formatted 100-page deck, tasks that will traditionally require a team of 5 consultants working 6–8 weeks can now be completed much faster. This leap in speed and breadth of output will unlock recent levels of productivity, allowing human experts to concentrate on high-level strategy and judgment.
AI will enable 50–100x more deals within the pipeline. By automating large parts of due diligence and evaluation, AI-driven solutions will help investment managers expand their deal-screening capability exponentially, uncovering more opportunities and diversifying portfolios in ways in which were previously unfeasible.
Probably the most pivotal element will probably be the amplified human-AI synergy. As these “super agents” tackle 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 subsequent five years.
As AI tools turn out to be more prevalent, how do you see human analysts and AI collaborating in the longer term?
As AI tools turn out to be more prevalent, the longer term of human analysts will revolve around collaboration reasonably than competition with AI. Reasonably than replacing analysts, AI will act as a robust augmentation tool, automating repetitive tasks and enabling analysts to concentrate on higher-value, strategic work. Probably the most successful analysts will probably be those that learn to integrate AI into their workflows, using it to reinforce productivity, refine insights, and drive innovation. Reasonably than fearing AI, analysts should view it as a game-changing tool that amplifies their skills and allows them so as to add greater value to their organizations.
Ultimately, AI won’t replace human analysts—but analysts who embrace AI will replace those that don’t.
