Building the Always-On AI Workforce
Rilo builds AI coworkers that execute real workflows across your existing tools, not suggestions but finished work.
Mission
Enable everyone to solve the problems that matter most to them. Rilo handles repeatable knowledge work so people can focus on the judgment, creativity, and decisions that actually move the business forward.
Operating Principles
The world is decorated in text.
Every business system exposes its intent through language: tickets, documentation, messages, and interfaces. This collapses the complexity of domain-specific automation and makes a single, general-purpose agent viable across every knowledge discipline.
Co-worker, not co-pilot.
The distinction is load-bearing. A co-pilot generates output you still have to act on. Rilo takes the task, executes every step end-to-end, and returns a finished result. We measure success by work completed, not tokens generated.
Each run teaches the next.
Every execution, successful or not, is a lesson. Rilo records what it learns: which paths worked, which shortcuts are reliable, and what your team's preferences are. That knowledge compounds: the 100th run of a task is qualitatively faster and more reliable than the first. The flywheel is the product.
Any tool a human can use.
Rilo reaches any web application, internal system, or browser-based workflow. No API integrations required, and no custom connectors. Structured APIs accelerate execution when available; browser automation is the universal fallback. No integration gap can block a task.
Correct behavior is the default path.
Fail-closed access, audit logging, and human approval gates are not compliance checkboxes. They are the default state. The architecture is designed so a misconfiguration produces a blocked task, not a data leak. Enterprise trust is not added on; it is structural.
Intelligence is a commodity. Execution knowledge is the moat.
Model capability is no longer the bottleneck. Rilo is model-agnostic by design. It routes to the best available model, upgrades transparently, and does not bet on any single provider. What compounds over time is execution knowledge: battle-tested solutions, learned failure modes, and organizational context that no model switch can displace.
Join us
We are a small team solving hard problems at the intersection of agent orchestration, distributed systems, and machine learning. If that is where you want to spend your time, we want to hear from you.
View open positions