Kinesthetic
Kinesthetic enables continuous improvement for AI agents in specialized domains by letting them learn from unstructured feedback and historical experience. The company builds ergonomic interfaces on top of research methods that keep knowledge in natural language, making it auditable and portable across any model. This approach removes the need for ML expertise or extensive reinforcement learning environments.
- Artificial Intelligence
- AI Agents
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
AI agents in specialized domains often fail to improve at a meaningful rate because current methods rely on ad-hoc system changes, extensive reinforcement learning environments, or verifiable reward signals that cannot capture the nuanced failure modes of complex real-world work. Domain experts and operators lack accessible ways to teach agents from unstructured feedback and historical experience, limiting the long tail of specialized domains from benefiting from capable AI systems.
Solution
Kinesthetic enables continuous agent improvement by leveraging optimized processes around data already available for capture and human-agent collaboration. The company builds ergonomic interfaces on top of research methods that let agents learn from any feedback they receive from users, engineers, operators, and their own experiences. This approach allows agents to improve at a rate significantly faster than today's go-to processes, without requiring ML expertise or extensive up-front investment. A natural byproduct of the system is portable, auditable knowledge that can be transferred across any model or agent in an organization's ecosystem.
Target Audience
Leadership teams seeking to increase the rate of improvement and capability extension of their AI agents, particularly those operating in specialized domains where verifiable reward signals are inadequate.
Features
- Research methods that combat failure modes beyond those optimizable against verifiable rewards, targeting specialized domains where reward signals are insufficient
- Knowledge representation in natural language that can optionally be distilled into model weights, preserving auditability and portability
- Learning from unstructured feedback and the agent's historical experience without requiring extensive RL environments
- Ergonomic interfaces that enable end-users, operators, and experts to shape agent behavior without extra steps or ML expertise
- Transferable knowledge assets that can move across any model or agent in a customer's ecosystem