Reward design is too consequential to automate. Our experts define task logic, design reward rubrics, and annotate trajectories.
Train agents around your goals through realistic tasks that reflect real-world tools, data, uncertainty, and decision-making.
Flexible integration and secure deployment options designed to fit your stack.
Train, test, and trust agentic AI on real work.
Tasks are drawn from work that creates real value like coding, accounting, banking, legal and compliance. Agents learn to complete the work, not just pass the test.
Measure success with built-in rewards, rubrics, and automated checks. Every outcome is auditable, reproducible, and verifiable.
The most complex tasks need a reference. Human annotations define what good looks like and teach models the moves reward signals alone can’t.
Every run is logged and replayable. Debug failures, compare model versions, and show stakeholders exactly what the agent did and why.
Start with simple tasks, layer in constraints, and increase complexity over time within the same environment. No rebuild required as capabilities improve.
Tasks are drawn from work that creates real value like coding, accounting, banking, legal and compliance. Agents learn to complete the work, not just pass the test.
Measure success with built-in rewards, rubrics, and automated checks. Every outcome is auditable, reproducible, and verifiable.
The most complex tasks need a reference. Human annotations define what good looks like and teach models the moves reward signals alone can’t.
Every run is logged and replayable. Debug failures, compare model versions, and show stakeholders exactly what the agent did and why.
Start with simple tasks, layer in constraints, and increase complexity over time within the same environment. No rebuild required as capabilities improve.
An RL environment is where an agent learns to work. It defines the tasks, the rules, the feedback signals, and the consequences of every decision. Get it right, and your agent learns to do the work. Get it wrong, and it learns to game the environment instead. Most RL environments are built around proxies – simplified stand-ins for real tasks. Invisible Technologies builds them from actual workflows, with reward structures designed by domain experts. The result is an agent trained on what the work actually demands.
Reward design is the process of defining what an agent should be rewarded for, the foundation of any RL system. Reward shaping is a technique applied during training that introduces intermediate rewards to supplement the main signal, encouraging exploration and faster learning where feedback would otherwise be sparse.
Trajectory generation is the process of producing the sequences of actions and outcomes an agent learns from. Annotation is the process of reviewing those sequences and marking what good and bad decisions look like in context. Both matter. Generation without expert annotation produces volume without signal. Annotation without control over generation means you're working with trajectories that may not reflect the tasks your agent actually needs to master. Invisible Technologies handles both: domain experts design the tasks that produce meaningful trajectories, and annotate them to understand what ‘good’ looks like at every decision point.
Verifiable means every rollout is logged, inspectable, and replayable. You can see what the agent did, what reward it received, and at what decision point things diverged. That means you can debug failures, compare model versions, and demonstrate agent behavior to stakeholders without reconstructing anything from memory.
It comes down to how the environment was built. Agents are good at finding shortcuts to a reward. If the signal is even slightly misaligned with the actual task, they'll get to the outcome without doing the work. Invisible's environments are designed by domain experts who understand the failure modes, not just the task. Reward logic is grounded in what the work actually requires. Every run is logged and replayable, so you can see exactly what the agent did.
