Proper Motion
Proper Motion provides continual learning infrastructure that lets AI agents adapt based on real-world usage after deployment. The platform, Virgo, connects agent behavior metrics like latency and accuracy to business outcomes, traces performance degradation to root causes, and enables teams to test and deploy corrections. It closes the learning loop by measuring whether outcomes improved and feeding real usage data back into the system.
- Artificial Intelligence
- Developer Tools
- AI Agents
- Data & Analytics
- Software Only
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
AI agents are typically tuned once and released into environments that continually change. As real-world conditions shift, agent behavior degrades over time—latency, quality, and accuracy drift from what users need—because teams lack systematic ways to observe, diagnose, and correct post-launch performance gaps. This static deployment model forces teams to rebuild or retune from scratch rather than improving continuously.
Solution
Proper Motion provides a continual learning infrastructure platform that lets AI agents adapt based on how they are actually used. The platform connects agent behavior—including latency, quality, and accuracy—to business outcomes such as customer retention, giving teams visibility into what small shifts in performance cost them. When a degradation signal appears, the system traces it to likely causes and proposes corrections that teams can test and deploy. Once deployed, the platform closes the learning loop by measuring whether the outcome moved and feeding real usage data back into the system to guide further adjustments. The core product, Virgo, analyzes telemetry, traces, prompts, responses, and feedback from customer workflows to power this continuous improvement cycle.
Target Audience
Primary customers are engineering and product teams at organizations that deploy AI agents or model-driven features in production and need to maintain and improve agent performance continuously after launch.
Features
- Connects agent behavior metrics (latency, quality, accuracy) directly to business outcomes such as customer retention
- Automated detection and diagnosis pipeline that traces performance signals to likely root causes
- Correction workflow enabling teams to test and deploy fixes, then measure their impact on outcomes
- Closed-loop learning system that feeds real-world usage data back into agent behavior adjustments
- Privacy-first data handling with configurable ingestion modes, including redacted ingestion that filters sensitive content before storage
- Integration support for third-party products, model providers, repositories, and communication tools via APIs and SDKs