Forsy

Capture and sell your AI agent workflow data

Artificial IntelligenceData

▲ 98 votes5 commentsLaunched May 23, 2026

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Daily #6Weekly #89

Forsy captures workflow data from the agents you already use (OpenClaw, Claude, Codex, Hermes, etc.) and turns it into sellable structured data. It creates a marketplace for authentic, high-fidelity workflow data with licensing and privacy built in. Forsy is building the infrastructure for a new agent data economy, where real agent workflows become training data for RL and more capable future agents.

AI Analysis

📝 Summary

Forsy captures workflow data from AI agents like OpenClaw, Claude, Codex, and Hermes, converting unstructured interactions into sellable structured datasets. Core features include automated capture, data structuring, a dedicated marketplace, plus built-in licensing and privacy mechanisms. It solves key pain points: scarcity of authentic, high-fidelity agent workflow data for RL training and lack of ways for users to monetize their AI agent usage. USP is pioneering infrastructure for an agent data economy, turning real behaviors into training assets for future advanced agents. Value proposition: create a compliant marketplace for trading genuine AI workflows.

📈 Market Timing

In 2025-2026, AI agent adoption is accelerating with maturing frameworks and rising enterprise use cases, driving acute demand for real behavioral data beyond synthetic datasets for RL and model improvement. User demands are shifting toward data ownership/monetization, while economic investment in AI remains strong despite regulatory focus on privacy (which Forsy addresses via built-in controls). This aligns perfectly with the transition to agentic AI. Rating: Excellent Timing.

✅ Feasibility

Technical integration with diverse agents (Claude, etc.) and real-time structuring is moderately difficult; marketplace development adds cost for storage, search and transactions. Compliance risks are notable around data privacy, consent and licensing across jurisdictions. Scalability is high via cloud but depends on user adoption for data supply. Team fit in AI/data space would help. Overall rating: Medium due to integration and regulatory hurdles, though core tech leverages existing agent APIs.

🎯 Target Market

Main segments: AI/ML engineers, researchers and developers building or using agents (ages 25-40, tech-savvy); AI-first enterprises and startups in software, automation. Industries: Artificial Intelligence, data services. Geographic: primarily North America and Europe tech hubs, with growing Asia interest. TAM for AI training data ~$5-10B by 2027; agent-specific workflow data SAM ~$800M, early SOM ~$50-100M. Core pains: lack of real high-quality agent trajectories, ethical sourcing issues. Willingness to pay: high for licensed, privacy-safe premium datasets that boost model performance.

⚔️ Competition

Competition level: Medium. Direct competitors: 1. Scale AI (scale.com), 2. Surge AI (surgehq.ai), 3. Labelbox (labelbox.com), 4. Snorkel AI (snorkel.ai), 5. Humanloop (humanloop.com). Forsy advantages: automatic passive capture from existing agents, focus on authentic workflow (not manual labeling), built-in marketplace with licensing/privacy for monetization. Disadvantages: newer entrant with potentially narrower initial feature set, dependency on agent platform integrations, less brand trust versus established data platforms that offer broader services and enterprise contracts.

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