Bravo 01 Labs & Dynamics
Announcement · September 23, 2026

DIM-1: Bravo 01 Labs launches the First "Decision Intelligence Model"

Bravo 01 Labs introduces a new category of AI models built for structured decision intelligence — achieving 92.37% held-out accuracy on BANKING77, decisively outperforming leading non-LLM decision models.

Today, Bravo 01 Labs & Dynamics Inc. released DIM-1, its first Decision Intelligence Model (DIM) — a new model category designed specifically for structured decision intelligence rather than conventional language-model generation.

92.37%
DIM-1 Held-Out Accuracy
79.7%
Jev (Independent Eval)
95.8%
Top-1 Coverage, Security-Sensitive Authority Cases
100%
Deterministic Reproducibility Across Runs
Evaluation on BANKING77, the financial-services intent classification dataset. Protocols differed between evaluations, so this comparison reflects reported decision-accuracy results — not a claim of overall superiority across speed, cost, or calibration.

A New Model Category

DIM-1 is not a general-purpose conversational language model. It is a specialized Decision Intelligence Model that transforms structured or unstructured inputs into controlled decisions — decisions that can subsequently be evaluated against explicit rules, authority, and execution constraints.

The architecture is built on a core principle: intelligence and execution governance should be distinct layers.

↔
Governance Layer

Korda AI

Deterministic action governance — constraining what actions can be expressed or executed.

This separation allows a probabilistic intelligence system to make decisions while restricting what actions can ultimately be expressed or executed — a design intended for high-consequence environments where control matters.

DIM-1 Results

In its current evaluation, DIM-1 achieved:

These results are an initial demonstration of the DIM architecture. Bravo 01 Labs is continuing evaluation across additional datasets, decision domains, security-sensitive scenarios, and governance conditions.

The DIM-1 and Jev evaluations used different testing protocols. The comparison presented here is a comparison of reported decision-accuracy results, not a claim of overall superiority across speed, cost, calibration, or other dimensions.

Korda AI and Government-Ready AI

The DIM-1 release expands Bravo 01 Labs' broader AI infrastructure strategy. The company's Korda AI is an assessed Awardable solution through the Department of War's Tradewinds Solutions Marketplace — the U.S. government's marketplace for assessed AI/ML, digital, and data solutions, providing a pathway to government procurement.

Korda is designed as a deterministic governance layer for autonomous and AI-driven systems, using formal action constraints and permissions to prevent unauthorized actions from being expressible within the governed action language.

Bravo 01 Labs is now pursuing an Awardable implementation pathway for DIM, extending the company's government-oriented AI architecture beyond action governance into decision intelligence.

The launch of DIM-1 and the creation of a new category of model, Decision Intelligence Models, is an important step in building the next generation of AI in America. DIM-1 is only a small glimpse of what this architecture can become. We see it as a stepping stone toward a much larger intelligence developments we are working on at Bravo 01 Labs — Developmnents designed towards decision intelligence, governed capability, and systems that can operate with greater determinism and control. What we are releasing today is the beginning, not the destination.

Albert BravoFounder & CEO, Bravo 01 Labs

Beyond DIM-1

DIM-1 is the first public release of Bravo 01 Labs' Decision Intelligence architecture. The company is developing a broader AI infrastructure stack combining decision intelligence, deterministic action governance, sovereignty verification, modular intelligence, and locally deployable AI systems.

The longer-term objective: AI systems capable of operating across enterprise, government, research, and other high-consequence environments — where conventional generative AI architectures may not provide sufficient control over decisions and actions.