Vadim Zolotokrylin: The Battle for Truth in the Age of Autonomous Markets

Day 55 of 100 days of TRUF

Vadim Zolotokrylin, CEO & Co‑Founder at Holdex, has been in the trenches of building data systems long enough to know: bad data breaks everything. From trading algorithms to AI agents, when decisions are automated, trust in the underlying data becomes existential.

“You can’t automate financial decisions without knowing exactly where your data came from, how it’s been handled, and whether it’s been challenged against other sources.”

The Problem with Today’s Data

In financial markets, where every prediction is a bet, and every bet can cascade through a network of contracts, the stakes are high. Yet too often, data is opaque. Oracles pull in single feeds, with no transparency on provenance or quality. There’s no way to verify whether a number reflects reality or a corrupted source. Worse, there’s no mechanism to benchmark competing providers or to weed out bad actors without costly human intervention.

Vadim highlights another blind spot: speed.

“By the time data hits most blockchains, it’s already stale for a high-frequency trading bot or a real-time AI agent.”

As AI agents begin to execute trades, rebalance portfolios, or trigger payouts on behalf of users, the margin for error shrinks to zero. The system needs a way to:

• Cross-check data from multiple providers.
• Verify provenance, not just outputs.
• Reward accuracy and penalize low-quality feeds automatically.
• Deliver streams fast enough for real-time systems.

This isn’t optional; it’s foundational for the next era of machine-driven markets.

The TRUF.NETWORK Approach

Enter TRUF.NETWORK. Vadim is part of a wider team of builders shaping a system where data is delivered, but it’s also contested, verified, and continuously scored. Multiple providers publish the same data, their feeds benchmarked and normalized against each other. Consumers can:

• Build indexes that combine diverse sources for stronger signals.
• Audit every data point back to its origin.
• Avoid the trap of single-source failure.

“What excites me is seeing TRUF turn data into a marketplace where accuracy wins and bad data is naturally pushed out.”

It’s a paradigm shift: from static feeds to dynamic ecosystems where participants are incentivized to maintain quality or risk being priced out. And this isn’t just theory, terabytes of data are already flowing, with builders like Nuon.fi, Index.fun, and Truflation building real-world apps on top.

Familiar Tools Meet Next-Gen Infrastructure

With TRUF.NETWORK, developers don’t have to wrestle with blockchain complexity. SQL access means they can plug in their existing analytics tools and query terabytes of on-chain data in real time.

Vadim points out that even business analysts and power users outside of web3 can integrate TRUF data streams into their workflows without learning new languages or frameworks. It’s about making the network as accessible as it is robust.

He adds:

“We’re also exploring partnerships with big analytics platforms so that one day you could click a button in almost any existing data tool and pull in verified, on-chain data for your dashboards.”

Agents, Portals, and the Road Ahead

As Vadim sees it, AI agents will soon be the dominant interface to financial systems. But for those agents to make reliable decisions, they need a verified, trustless data layer. TRUF.NETWORK is preparing for this shift with:

• Portals - local nodes that deliver web2 speed and UX on top of a web3 foundation.
• Billing - creating a sustainable economy where the TRUF token mediates value between publishers and consumers.

“We want builders and innovators to come talk to us, test it, break it, and shape it. The more we collaborate now, the stronger the foundation for everyone.”

Build With Us, Shape the Future

The future of financial markets - and the agents that navigate them - depends on data integrity. Whether you’re a developer, analyst, or investor, now is the time to get involved. Help us pressure-test the system. Contribute your perspective.

Let’s set the standard for data provenance and verification together.