Primary Logic
Primary Logic provides an autonomous AI agent that handles unfinished revenue workflows—such as patient balance collection, bookings, and follow-ups—directly within a business's existing systems. The platform operates within established rules to complete tasks from initial contact through payment posting, enabling small teams to recover revenue without scaling headcount.
- AI Agents
- Artificial Intelligence
- Enterprise Software
- Healthcare Technology
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Running a business involves an enormous amount of operational work—building products, delivering services, managing finances, coordinating teams, and handling daily logistics—that consumes time and resources that could otherwise go toward customer relationships. Small teams are limited in how many customers they can serve and how much they can deliver without building larger organizational structures to support that growth.
Solution
Primary Logic is building infrastructure designed to enable artificial general intelligence to perform business operations across an entire organization. The company is creating systems that give intelligence access to a company's software and information, provide it with memory of past activities, and enable it to act autonomously. Their approach focuses on solving problems that only surface when AI does real work: keeping multiple agents coordinated, recovering from failures mid-task, and learning from outcomes that may take months to observe. This lets a small team serve more customers and deliver more value without needing to scale headcount proportionally.
Target Audience
Primary Logic targets business leaders and companies that want to deploy AGI-driven operations, as well as engineers and technical teams interested in building the underlying systems for autonomous business execution. The current homepage demonstrates a healthcare-focused use case serving medical practices that need to recover aged patient balances.
Features
- Cross-system integration layer that gives AI agents access to enterprise software and data repositories
- Persistent memory architecture enabling agents to recall prior work and maintain context across sessions
- Agent coordination framework for managing multiple AI agents working on interdependent business tasks
- Failure recovery mechanisms that detect and resolve errors when agents carry work across multiple systems
- Long-horizon learning system designed to improve performance based on outcomes observed over months-long cycles
- Task execution engine that can see a job through from initiation to completion over extended timeframes
- Automated patient balance explanation and collection in EHR/PM systems, triggered by age of balance and prior outreach attempts
- Multi-channel patient outreach via SMS, phone, and email, with preference learning for future communications