Hone
Hone builds persistent AI systems called Engines that own business outcomes rather than completing discrete tasks. These Engines self-onboard into a company, learn its systems and history, and self-improve by modifying their own tools and code—with changes verified through simulation-based replay before deployment. The platform includes a governance layer, organizational memory, and built-in evaluation to ensure reliability and auditability.
- Artificial Intelligence
- AI Agents
- Enterprise Software
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Frontier AI models have rapidly expanding capabilities, but most organizations still operate much as they did five years ago. A widening gap persists between what advanced AI can technically do and the economic value it actually creates, because companies lack the infrastructure to deploy autonomous systems on their hardest, most complex processes.
Solution
Hone builds the organizational and technical infrastructure to direct frontier intelligence at large-scale challenges. The company creates Engines, persistent AI systems that own outcomes tied to real economic value rather than completing discrete tasks. Engines onboard themselves into a company, learning its systems, people, history, and constraints to understand what good looks like, then proactively figure out what needs doing and execute it while respecting guardrails. Hone's platform includes a self-improving harness that lets engines modify their own tools, code, and memories, with simulation-based replay that verifies changes before they go live. The result is enterprise AI that compounds its intelligence over time, adapting to company changes and taking on new responsibilities without requiring constant human prompting.
Target Audience
Hone works with leading AI companies and some of the world's largest enterprises that need to deploy autonomous systems on their most complex operational challenges, targeting organizations ready to assign real economic outcomes to AI.
Features
- Persistent Engines that meet goals rather than completing tasks, staying focused on outcomes even when interactions end or circumstances change
- Self-onboarding capabilities that let Engines learn company systems, people, history, and constraints to understand context and what success looks like
- Controlled self-improvement loop where Engines modify their own tools, hooks, code, memories, and workspace context, with changes replayed in simulation before promotion
- Simulated replay technology using a "director" agent that mocks people and system events, with fast-forwarded time to test week-long workflows in minutes
- Built-in evaluation with defined metrics, testing changes against past cases in simulation to cut regressions from 33% to 5% across 125 scenarios
- Governance layer providing custom policies for sensitive actions, full audit logging, and control over how Engines are used
- Organizational memory that stores user preferences and company best practices across people and runs, updating as the system learns
- Engine repository where Engines implement themselves, writing routines that handle their work in versioned code that can be reused