Kerva
Kerva is building toward autonomous product creation: a system that can find problems worth solving, shape solutions, build software, take it to market, and improve from real use. The company learns by shipping real products, then captures the expert context, decisions, and taste loops that can be handed to AI agents. Humans set taste and constraints while agents expand the surface area the team can inspect, produce, test, and refine.
- Artificial Intelligence
- AI Agents
- Developer Tools
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Product creation has traditionally depended on founder instinct, small-team coordination, craft, luck, and manual operating work. This makes the process difficult to scale, repeat, or systematize, limiting how quickly software can be built and improved in response to real user needs.
Solution
Kerva is building toward autonomous product creation: a system that can find problems worth solving, shape the right solution, build software, take it to market, and improve from real use. The company's thesis is that much of product creation can be made explicit—the expert context, decisions, constraints, tests, feedback, and taste loops that turn a need into a product. Kerva learns by shipping real products, then uses each product to identify what can be captured, evaluated, repeated, and handed to agents. Humans set taste, constraints, positioning, and standards, while agents expand the surface area the team can inspect, produce, test, and refine. When parts of the work repeat, Kerva moves them into agents, tools, evaluations, workflows, and shared context so each next product starts with more of the system already in place.
Target Audience
Kerva's primary customers are organizations and teams seeking to systematize and automate software product creation, particularly those operating in AI-native environments where products are operated by AI systems and embedded in agent-coordinated workflows.
Features
- Autonomous product creation pipeline covering need-finding, solution shaping, software building, go-to-market, and improvement from real usage
- Explicit capture of expert context, decisions, constraints, tests, feedback, and taste loops for repeatable product development
- Agent-driven expansion of production, inspection, testing, and refinement capabilities
- Progressive automation where repeated work is moved into agents, tools, evaluations, workflows, and shared context
- Focus on building products for real use to identify what can be systematized and handed to AI systems
- Products designed to be operated by AI systems, exposed to agents, and embedded in agent-coordinated workflows