DDScore CEO Mikko Heilimo Joins InderesPodi to Discuss AI in Investment Analysis
A discussion on where AI helps investors, where generic models fall short, and why structured analysis matters for private-company and IPO-stage decisions.
DDScore Founder and CEO Mikko Heilimo joined Kasper Mellas on InderesPodi to discuss how AI can support investment analysis, why generic frontier models are not enough on their own, and how structured, probability-based analysis can help investors evaluate private companies and IPO-stage opportunities.
DDScore Founder and CEO Mikko Heilimo appeared on InderesPodi with analyst Kasper Mellas to discuss how artificial intelligence is changing investment analysis and decision-making.
Inderes is one of Finland's most respected investing platforms, combining equity research, investor content, listed-company communication, and an active investor community. Inderes describes its mission as democratizing financial information by connecting investors and listed companies, and more than 500 listed companies in Europe use its investor relations products and research services.
The discussion focused on a practical question: what can AI actually do for investors, and where are its limits?
Mikko explained that investment analysis should not be reduced to asking a large language model whether a company is a good investment. Generic frontier models such as ChatGPT, Claude, and Gemini can be useful, but they are not a complete investment analysis process. They can summarize, structure, and assist with research, but they also have weaknesses: they may miss changing market context, produce plausible but unsupported conclusions, struggle with calculation, or fail to distinguish clearly between submitted material and external evidence.
A key theme in the conversation was control. Mikko explained that serious AI-assisted investment analysis requires a defined process: what data is collected, which sources are trusted, how claims are checked, how risks are scored, and how outputs are reviewed. In his view, the strongest systems combine language models with other AI methods, structured data, verification steps, and human judgment.
This is also the foundation behind DDScore.
DDScore is built for private-company investments, early-stage companies, and IPO-stage opportunities, where investors often face incomplete information, optimistic pitch materials, and limited public data. The platform analyses pitch decks and supporting investment materials across 12 investor-focused dimensions, using deep search, real-time public data, and DDScore's Probability Math AI model to produce a 0-100 Due Diligence Score and a structured report.
In the episode, Mikko also highlighted why private-company analysis is different from public-company analysis. Listed companies usually have public financial statements, analyst coverage, market pricing, and continuous disclosure. Private companies often do not. The investor has to evaluate the quality of the team, the market, the business model, the valuation, the competitive position, and the missing information before a clear market signal exists.
That is where structured AI can add value: not by replacing investor judgment, but by making the first layer of analysis more systematic, evidence-based, and current.
Thank you to Kasper Mellas and Inderes for having Mikko as a guest and for hosting such a thoughtful conversation on AI, analysis, and the future of investment decision-making.
The episode is in Finnish. Non-Finnish speakers can use YouTube subtitles and auto-translation.