Boltz AI™ Reasoning Engine

Reason at Scale. Simulate Agents. Infer Strategies.

Boltz AI™ Reasoning Engine helps you discover optimal decisions and equilibrium behavior in complex, multi-agent environments.

Why the Reasoning Engine?

Traditional models are designed to optimize a single decision at a time, often under fixed conditions and with a narrow focus on one metric. In contrast, most real-world systems involve multiple objectives and KPIs, several interacting agents, and complex trade-offs and constraints.

The Boltz Reasoning Engine uses generative AI to simulate optimal decisions in complex, multi-objective environments, including settings where multiple agents interact, coordinate, or compete.

From competitive markets to multi-functional workflows, the engine offers a principled way to discover emergent behavior, optimal decisions, and collaborative strategies, all without building rule-based logic or custom models.

What It Enables

  • Multi-Agent Simulation: Model how competing or collaborating agents behave in shared environments.
  • Multi-Objective Decisions: Find optimal decisions in multi-objective or cross-functional settings such as sales, marketing, logistics, finance, and enterprise operations.
  • Optimal and Reactive Planning: Use Boltz AI™ not only to optimize decisions based on explicit goals, constraints, and trade-offs, but also to react to changing conditions and continuously reoptimize as the environment evolves.
  • Domain-Specific Reasoning with LLMs: Use Boltz AI™ to build tailored reasoning engines that enrich large language models with structured, domain-specific logic.

Application Areas

Marketing Optimization

Boltz AI™ enables full-funnel optimization of marketing strategies and tactics, balancing performance trade-offs from brand awareness to conversion.

Supply Chain

Optimize joint supply strategies between partners and simulate cascading effects of disruptions, demand shifts, or incentive structures across the value chain.

Business Strategy

Support strategic business decisions by optimizing trade-offs, constraints, long-term outcomes, and cross-functional resource allocation.

Customer Journey

Model the full customer journey across channels and stages to develop strategies that improve experience, reduce churn, and increase lifetime value.

Agentic AI Coordination

Train Boltz to coordinate self-directed LLM agents in multi-stage workflows, ensuring consistency and shared objectives.

Product & Service Offering

Determine optimal offering strategies based on customer context and product attributes, optimizing personalization, bundling, and positioning.