Explainability has become one of the defining challenges in regulatory technology. As compliance engines scale to millions of decisions per day, firms (and supervisors) are no longer satisfied with binary answers alone. They need to understand why a rule applied, how a conclusion was reached, and where that logic traces back to the source text – quickly, consistently, and in a form that can be shared across operations, compliance, and audit teams.
That is the problem Droit, a technology firm at the forefront of computational law and regulation, is targeting with the launch of Decision Decoder. This new capability adds natural-language explanations to the regulatory decisions produced by Droit’s Adept Platform. Decision Decoder focuses on making those outcomes understandable – turning structured decision paths into readable, contextual explanations that remain fully traceable and auditable.Explaining Decisions Without Changing Them
Adept encodes regulatory obligations as structured decision trees, with every node explicitly linked to annotated regulatory source text. What Decision Decoder adds is a layer of interpretation – summarising how a specific set of inputs travelled through that decision space and why the resulting outcome was reached.
As Droit’s founder and CEO Brock Arnason explains, the underlying logic remains untouched. “The decision logic doesn’t actually change. You get the same decision back, driven by the same structured data. You get the same traceable audit record. What you also get is a decoded summary of exactly how that decision was arrived at, using the facts that were specific to that particular message call.”
This distinction is critical. Droit is deliberately limiting the Decision Decoder role to explanation. The canonical answer – the regulatory determination itself – continues to come from Adept’s symbolic logic models.
Why Symbolic Logic Still Matters
To understand why this approach matters, it helps to look at how Adept was built. Since its inception, the platform has been grounded in symbolic logic rather than probabilistic machine learning. Regulatory texts are normalised, versioned, and converted into structured data, then expressed as deterministic decision trees that can be rendered visually and audited precisely.
This architecture has allowed Adept to scale across hundreds of global regulatory mandates while meeting supervisory expectations around transparency and traceability. It also means that every decision produces a durable audit record: the version of the rule applied, the path taken through the logic, and the specific regulatory provisions that informed the outcome.
However, that same precision can create usability challenges. Navigating complex decision trees requires familiarity with regulatory logic, and explaining outcomes to non-specialists can be time-consuming. Decision Decoder is intended to close that gap.
Using GenAI Where It Works Best
Droit’s use of large language models is deliberately constrained. Rather than asking an LLM to reason about regulation in the abstract, Decision Decoder feeds it a tightly bounded set of inputs: the traversal path through Adept’s decision tree, the annotated regulatory text associated with each step, and the facts supplied in the query.
“The reason why it works so well is because it does what LLMs do best, which is to summarize structured text given strong guardrails and specific guidance in the context window. It does a great job,” Arnason says. The guardrails come from the Adept structured decision path.
This approach reflects lessons learned during earlier LLM experimentation. In isolation, generative models struggled to deliver consistent, reliable regulatory explanations. When anchored to Adept’s structured logic, the results became not only more accurate but also operationally useful at scale.
Importantly, the explanation is always additive. “You can never say that what an LLM provides is 100% correct,” Arnason acknowledges. “But you can get a very high reliability over many sample requests. And the beauty of this is that you never leave the canonically correct answer.”
From Transparency to Decision Legibility
In practice, Decision Decoder is about reducing friction in day-to-day compliance workflows. Instead of requiring users to step through decision trees or cross-reference digital libraries, the platform surfaces a readable explanation upfront – complete with links back to the underlying audit record.
“You can take the explanatory text, cut and paste it to an internal chat, or send it in an email with a link to the actual audit record,” Arnason notes. “It greatly simplifies the process of explaining a decision.”
This has implications beyond efficiency. By lowering the barrier to understanding, Decision Decoder broadens access to regulatory logic across operations teams, subject matter experts, and compliance leadership alike. As Arnason puts it, “It improves decision legibility. And it just makes it so much easier for people with varying levels of expertise to engage with and understand decisions as part of their daily workflow.”
That legibility also matters in regulatory interactions, where firms are increasingly expected to demonstrate not just compliance outcomes, but the reasoning behind them.
Building on the Adept Foundation
The launch of Decision Decoder builds directly on capabilities already embedded in Adept. The platform’s Digital Library enables firms to compare regulatory versions, inspect annotations, and understand how changes in law propagate into decision logic. The Logic Viewer visualises the exact path taken through a regulatory decision tree.
Decision Decoder effectively acts as a bridge between those tools – translating structured logic into narrative form without losing the connection to source text. It is an incremental extension of Adept’s design philosophy rather than a departure from it.
Looking Ahead: Droit’s 2026 Roadmap
While Decision Decoder is focused squarely on explainability, Droit is already exploring adjacent use cases for generative AI – “carefully and incrementally.” One area under consideration is using LLMs to help populate structured inputs from natural-language scenario descriptions, with a human firmly in the loop. Another is the idea of a “synthetic knowledge engineer” interface that allows users to ask questions about regulatory logic and scenarios conversationally.
These initiatives sit on what Arnason describes as a “spectrum of reliability,” with Decision Decoder positioned at the most robust end – fully tested and anchored to the canonically correct answer.”
In a compliance environment where trust and explainability increasingly define technology choices, Decision Decoder signals a pragmatic path forward: not replacing rules with AI but making rules – and the decisions they drive – clear enough for humans to trust.
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