Why Accuracy Isn’t Optional: The Future of Regulatory Technology Demands a Better Kind of AI

Leading with Perspective, Leila Banijamali

We live in an age of AI hype. ChatGPT generates prose with surprising eloquence. Generative models power vivid cinematic experiences at little cost to creators. Predictive systems optimize supply chains, place market orders, and personalize content feeds. The technology landscape is electrifying, and with good reason – these innovations have catalyzed genuine breakthroughs across industries.

But here’s what troubles me: as we’ve built Symbium into a leading regulatory technology company that has fundamentally reimagined how citizens interact with government –starting with permitting –I’ve watched the market conflate different types of AI without understanding their appropriate applications and, critically, their limitations.

I was recently at an industry conference where a founder of an AI-enabled plan checking company declared that “a good AI means that the permit is almost stampable.” I would characterize that statement as precisely backwards. A permit that is “almost stampable” is rife with problems waiting to be uncovered as a matter of safety. That’s not good AI –not by any stretch.

And here’s the real question: why accept “almost stampable” when we can build systems that deliver genuinely stampable permits? Why settle for an AI that requires human review to catch the errors it’s certain to make, when other systems can achieve the accuracy that enables permits to be issued instantly, stamped without delay?

The Built Environment Cannot Afford Ambiguity

When a homeowner installs a rooftop solar array, that installation must comply with electrical codes to prevent fires. When a contractor adds a new service panel to a house, it must meet grounding specifications to protect residents from electrocution. When an accessory dwelling unit is constructed, it must satisfy setback requirements, occupancy limits, and fire safety codes to ensure the neighborhood remains safe and livable.

These aren’t abstract requirements. They’re written in blood. Every electrical code exists because someone was injured or killed before that code was written. Every fire safety standard emerged from lessons learned in tragedy. The built environment is fundamentally different from other domains –it’s where people live, raise their families, and invest their life savings.

When we ask technology to verify compliance with these codes, we’re not asking it to be “pretty good.” We’re not asking it to be right 85% of the time. We’re asking it to be right 100% of the time. Because 95% accuracy means tens of thousands of non-compliant installations across a state of 40 million people– installations that will exist for decades, creating liability and safety risks. Multiply that by the cascading costs: remediation, legal liability for municipalities, reputational damage to contractors, and eroded public trust in clean energy. These aren’t edge cases. They’re inevitable failures baked into systems that accept probabilistic accuracy. That’s untenable.

And yet, the permitting technology space is flooded with AI-enabled systems that don’t deliver this standard.

Understanding the Two Kinds of AI That Matter Here

The hype around AI today obscures a critical distinction: not all AI systems are created equal, and their suitability depends entirely on the problem they’re solving.

Predictive AI systems learn patterns from vast datasets and generate probabilistic outputs. ChatGPT is the quintessential example. Ask it the same question four times, and you’ll receive four different answers – each plausible, but none guaranteed to be correct. These systems are extraordinary for their versatility and creativity. They’ve powered everything from drug discovery to impressive content generation. This form of AI has enabled a global revolution in productivity.

But here’s the catch: predictive systems asymptotically approach accuracy. You can never guarantee 100% correctness. By their mathematical nature, they trade certainty for flexibility.

When AI-enabled plan review services have entered the market in recent years, many have adopted predictive approaches. Some proudly advertise that their systems achieve 95% accuracy. Others require 10 days to generate a single analysis. (Let that sink in: a system that takes 10 days and still isn’t guaranteed to be correct.) These services are built on the wrong foundation for building code compliance.

The question isn’t whether predictive AI is useful. It is. The question is whether we can–and should–do dramatically better in the built environment. The answer is yes.

Where Deductive Systems Reign Supreme

Deductive AI systems reason from known knowledge rather than patterns in historical data. Building codes are fundamentally deductive problems. The rules are explicit. The logic is defined. There’s no uncertainty about whether a solar installation meets electrical code –either it does, or it doesn’t. The knowledge that answers that question isn’t hidden in data; it’s written in municipal ordinances and engineering standards.

Symbium’s Complaw® technology is a knowledge-based deductive system. It doesn’t predict; it deduces. It applies logical reasoning to explicit regulatory knowledge and produces 100% accurate compliance analyses, every single time. There is no 95% accuracy. There is no 10-day wait. There is only certainty.

Deductive systems like Symbium also offer something else that predictive systems fundamentally cannot: reliable transparency. It doesn’t hand you a binary yes-or-no answer and ask you to trust the black box. You can see exactly why a particular decision was made. The legal logic behind the analysis is always available, always transparent, always 100% correct.

This changes everything for building departments, contractors, and property owners. No more wondering how a decision was reached. No more questioning whether some hidden AI logic made an error. The reasoning is laid bare, explainable, and verifiable.

This is what the built environment demands.

The Scalability Story

Deductive systems, when designed properly, are remarkably scalable. The same logical architecture that powers instant permit decisions for solar installations can be adapted to verify loan eligibility requirements for mortgage lending, to analyze insurance claim requirements and coverage policies, to assess tax implications of property modifications, and to validate regulatory compliance across virtually any domain where rules are explicit and knowledge is defined.

This is the future: a world where regulatory analysis – no matter how complex or arcane – is available instantly, transparently, and with guaranteed accuracy.

But this is just the beginning.

The Road Ahead

The AI revolution is real. But not every problem should be solved with predictive systems. The built environment – and regulatory analysis more broadly – demands accuracy without compromise, transparency and reliable explanations without exception, and systems built on logic and deduction rather than statistical probability.

For those interested in the deeper academic foundations of this approach Stanford Professor and Symbium co-founder, Michael Genesereth, is the leading researcher in computational law. His seminal paper, The Cop in the Backseat, is a beautifully explained introduction to computational law intended for a broad audience.

The permitting industry is just the beginning. The question we should all be asking is: what else are we settling for 95% accuracy on, when we could be building better?

Leila Banijamali is the CEO and Co-Founder of Symbium and a former Stanford CodeX Research Fellow. Symbium is a regulatory technology company that uses computational law to automate compliance verification and enable instant permitting. Symbium serves over 270 jurisdictions across California and Colorado and is expanding nationally.