Perpetual Labs
Perpetual Labs provides a two-component platform that converts an enterprise's daily operational workflows—emails, judgment calls, and actions—into structured training data for AI labs, with strict privacy controls that let companies approve every dataset before it's sold. The platform also includes PerpetualOS, a runtime that deploys post-trained models to automate repetitive tasks and feeds new traces back into the system for continuous improvement.
- Artificial Intelligence
- Enterprise Software
- AI Agents
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
General-purpose frontier AI models excel at broad internet-derived tasks but often underperform in specialized enterprise workflows where context, historical decisions, and institutional knowledge are crucial. Companies lack a systematic way to convert their own operational expertise into models that reason and act with domain-specific accuracy.
Solution
Perpetual Labs offers a two-component platform that turns real work into training data and then into operational AI. The Foundry captures traces from an organization's daily activities—workflows, judgment calls, and actions—and converts them into post-training assets. These assets fine-tune a model that then runs through PerpetualOS, automating repetitive tasks with higher speed, lower cost, and greater accuracy than frontier models in that domain. Each action the model takes writes a new trace, which feeds back into the Foundry, creating a continuous improvement loop where each deployment generation produces a sharper, more capable model.
Target Audience
Large enterprises and Fortune 500 companies with complex, knowledge-intensive operational workflows—such as renewals, operations reporting, and routing—that can benefit from automation without sacrificing human oversight.
Features
- Trace capture and conversion: Raw operational workflows are transformed into structured training assets for model post-training.
- PerpetualOS runtime: Deploys the post-trained model to automate real-world tasks and records new traces from every action.
- Continuous model improvement loop: Each deployment cycle sharpens the data, post-trains a sharper model, and expands the scope of automated work.
- Versioned model ladder with accuracy tracking: Models iteratively deployed with measurable accuracy and scope, enabling human review of draft outputs before acceptance.
- Domain-specific performance: Faster, cheaper, and more accurate than frontier intelligence in the customer's operational domain.
- Privacy controls: Customers approve every dataset before it's sold; names, company names, contact details, and exact amounts are replaced while contextual meaning is preserved.