Episode Details

What It Takes to Build AI That Is Accurate Enough, Traceable Enough, and Trustworthy Enough for High-Stakes Financial Work

Guest: Nikita Komarov, CEO and Founder at Dobs.AI

Host: Seth Earley, CEO at Earley Information Science

Published on: September 9, 2026

In this episode, Seth Earley speaks with Nikita Komarov, CEO and Founder of Dobs.AI, who spent seven years at McKinsey advising Fortune 1000 executives before founding a company that is rebuilding financial due diligence, internal audit, and vendor overpayment recovery from the ground up as agentic AI systems. They explore why financial professionals are the most resistant to AI adoption and why that resistance is rational, how orchestrating teams of AI agents with financial controls built in produces outputs that are deterministic enough for audit, why the difference between an efficiency tool and a production-ready AI system is enormous, and how the trusted advisor status accountants have built over decades becomes a platform for entirely new services in the AI era.

Key Takeaways:

• Financial professionals are among the most resistant to AI adoption for a rational reason - LLMs are non-deterministic by nature, and accounting requires numbers that are 100% accurate and traceable.
• Building production-grade financial AI requires three levers working together: orchestrating teams of agents with defined roles, building financial controls and guardrails into the pipeline, and solving for data extraction accuracy before any analysis begins.
• The difference between an efficiency tool like Claude or ChatGPT and a production-ready AI system is not the model - it is the architecture, the controls, and the product thinking required to get from unstructured input to a final output a human can take to a client.
• DOBS AI compresses financial due diligence from a six-week engagement to 72 hours for the management meeting - cutting the cycle from week and a half to three days on that critical milestone alone.
• Accounting firms have more trust with clients than management consultants or lawyers, and that trust combined with recurring access creates a platform for expanding into advisory services that AI now makes possible.
• The pricing model reckoning is real - time and materials no longer makes sense when AI does the work in hours, and firms need to shift to value-based pricing anchored to the outcome delivered, not the hours spent.
• The long-term trajectory is positive, but the mid-term transition is the risk - AI is compressing decades of technological change into five to ten years, and organizations and individuals who are not adapting will be left behind.

Insightful Quotes:

"Large language models, they predict the next word. That's why these systems are non-deterministic. You can't say what the output will be next. That's the problem in financial services - you need 100% accuracy, but you don't know what the system is going to tell you." - Nikita Komarov

"That's exactly the difference between an efficiency tool and a production-ready solution. When people say we use AI, they most likely mean Copilot or ChatGPT - and that's 5 to 10% of what's actually possible." - Nikita Komarov

"You can't automate what you don't understand. The first thing you have to do is say, what is the expected output and the outcome, and then how do I verify that I actually get there?" - Seth Earley

Tune in to discover why financial AI is one of the most demanding and highest-stakes applications in the enterprise - and what it actually takes to build systems that are accurate and auditable enough to trust.

Links

LinkedIn: https://www.linkedin.com/in/nikita-komarov/

Website: https://dobs.ai

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