Inside Comp AI: How Min Chun Fu Is Redefining AI Compliance

Companies seeking to work with large enterprises must demonstrate that their systems protect customer data, follow required controls, and operate with consistent oversight. These standards determine whether a product can be adopted, integrated, or trusted at scale. As a result, they're a core requirement for any organization hoping to move into regulated or security-sensitive markets.

The challenge is that meeting these standards can take far too long, slowing sales cycles and draining resources from companies that need to move quickly. New tools in the realm of AI can reduce the manual work that comes with these to ease that burden, but only if teams understand how these systems reach their decisions. Without that visibility, speed alone isn't enough to earn confidence.

Comp AI is built around that problem. The platform relies on automated agents that surface every step of their checks to give organizations direct insight into how results are produced.

At the center of that approach is founding engineer Min Chun Fu, who directs how the system communicates its actions through design and interface decisions. His work is built around transparency, believing automation only works when users can see exactly what the system is doing and why.

Rethinking the Compliance Problem

Companies, especially tech-related ones, must typically meet standards like SOC 2 and GDPR to meet to demonstrate that their systems are responsibly managed. SOC 2, for example, checks how companies protect customer data across controls like access permissions and change management. GDPR, on the other hand, oversees how international companies collect and store personal information. These requirements have become prerequisites for enterprise partnerships, sometimes being a key determinant of whether a contract can move forward.

Automation offers a way to compress that timeline, as it can take care of the manual work once handled through screenshots, spreadsheets, and repeated human checks. But delegating high-stakes decisions directly to AI brings its own pressures, especially when the system's reasoning isn't visible. It's because of this uncertainty that recent industry surveys show that 56% of CEOs say they're not ready to plug AI directly into compliance workflows.

Min Chun Fu sees that reluctance as a sign that the infrastructure around automation hasn't caught up with its potential. He argues companies can properly AI only once they can follow each step of an automated check and understand how a result was produced. "Other providers spend months helping you submit evidence. AI can do it in hours—but only if people trust what it's doing," he says.

Comp AI: A Company That Rejects the Black-Box Method

Comp AI approaches this challenge by allowing users to run audits through natural-language prompts. Users simply ask the system, for example, to confirm whether two-factor authentication is enforced across Google Workspace. From there, the system creates the scripts and runs them inside a secure sandbox, producing results in real-time without the need to keep manual, back-and-forth verification. The end goal is that each step becomes observable as it happens, giving companies immediate, verifiable evidence instead of waiting for human review.

As Fu explains, "You can prompt to audit, and we show you the sandbox and the exact process. Users should know what's happening, not just hope."

With this design in place, the platform can run a large number of simultaneous checks across different environments at once, validating policies that previously required coordination among several specialists. Tasks that once needed long evidence chains can now be confirmed in minutes, and companies can be reassured that, despite the speed, they can trust the final output because they can trace it.

Min Chun Fu's Task: Setting Up a Design That Anyone Can Follow

As co-founding engineer, Fu approaches product design at Comp AI as the foundation of the platform's credibility, with each screen built to make clear what the system is doing and why. That structure reflects his belief that design is inseparable from how far users can trust these automated checks.

Visual transparency reinforces that principle by showing bits of code, displaying action logs, and updating users with clear system steps as they happen. The intention is to remove uncertainty through direct evidence. As he puts it, "AI is a black box to many. My approach is to make everything easy to understand."

Interface aesthetics add another layer to how Comp AI earns user confidence, with clear layouts that show each process directly and data presentations that aim to imitate how an engineer would review an audit manually. Consistent spacing, readable logs, and unobstructed system views become part of how organizations gauge the platform's trustworthiness.

His work on the front- and back-end of the platform makes this level of detail possible. He oversees the logic that gathers evidence, the flows that surface it, and the UI patterns that frame it, giving him control over how transparency appears end to end. The responsibility spans everything from keeping sensitive credentials safe to configuring how audit results materialize on the screen.

The fast pace of iteration heightens the weight of each decision, especially when changes can affect the entire platform. Revisions must remain reversible, stable, and legible to users who depend on accurate checks. "As a founding engineer, every change matters; you hesitate before pushing anything live. But that's the fun—that's the risk," he says.

Making AI Transparent as an Industry Standard

By working to reduce the time spent on compliance checking, Comp AI's goal is to help companies preserve their runway, speed up the time-consuming aspect that can halt deals, and monitor their workflows nonstop. For industries that depend on regulatory verification, this change alters how teams think about readiness and risk.

Fu believes the deeper shift is cultural. As AI becomes embedded in enterprise workflows, compliance is a domain that comes with a higher standard for accountability than most. This means that the expectation that automated systems reveal their actions in detail may (and according to Fu, should) become a baseline requirement across enterprise software.

Min Chun Fu's work at Comp AI shows a way forward for that future, one where companies can incorporate AI without fearing they're putting user trust on the line. For a field defined by dense regulations and cautious decision-making, the perspective suggests a path forward where companies can rely on AI without worrying about potential misuse, leading to this technology becoming even further incorporated into regular workflows.