LithiumQ
Building Enterprise AI systems that act confidently
in real-world environments.
Confidence is not free. It has to be earned.
Talk to UsThe Challenge
AI agents must make decisions
before they fully understand their environment.
- Signals are incomplete.
- Feedback is late.
- Short-term success hides long-term failure.
Acting today from prompts is easy.
Acting confidently over time is not.
Where AI Agents Fail
Agents observe sales, track inventory, and place orders.
Stockouts hide true demand.
Suppliers respond with delay.
Early optimization looks profitable.
Later, performance drops - the agent has stopped adapting.
Agents see conversions, revenue, and churn, and make pricing changes.
Price changes shift behavior.
Users adapt.
Competitors respond.
Short-term success. Long-term trends are mislearned. Sales decline.
Agents take actions and observe outcomes.
Early behavior looks correct.
Local symptoms are addressed.
Learning converges too quickly.
Early success. Later, agents act on beliefs that no longer hold.
Different systems.
Same failure mode.
Our Position
Building agents that act correctly today is not enough.
Before an agent can act confidently over time,
it has to learn how the system behaves.
Sometimes that means changing it.
Sometimes that means simulating it.
Both have cost.
Both require commitment that holds over time.
Current Status
LithiumQ is in an early research and system-design phase.
We are building environments
to study how AI systems earn confidence over time.
Some of this is still unresolved.
That is the point.
We are not yet offering products or services.