Models keep getting better at reasoning, generating, and acting. What they still miss is the person on the other side — whether the user is moving forward, stuck, or changing direction while the work is happening.
Qualia-1 gives any AI agent that missing context, so it finishes more tasks, needs fewer corrections, and earns repeated use.
Most agents see the request, the documents, the tools, and the system state. They know almost nothing about the user — whether someone is focused, stuck, correcting them, or quietly giving up. So adoption stalls: the agent works once, but never becomes part of how people actually work.
For enterprise products the real bar isn't whether an agent works once — it's whether people return to it, trust it, and let it into the workflow. That's the part most agents miss.
An agent that can't tell when someone is stuck, distracted, or redoing its work another way can't reliably carry a task to done. So it leans on the user to correct and re-explain.
Product teams see prompts, outputs, tool calls, and latency. They don't see what happened between the agent's response and the user's next move. That gap hides why an agent gets adopted, ignored, or abandoned.
Qualia-1 is an on-device model that gives AI agents structured context about the person using them.
It turns what's happening in the session into a compact human-context output any AI product can use during the interaction — not after it.
It doesn't replace the agent — it adds the one thing they're missing: what's going on with the user while the work is happening.
Four agents, replayed twice: once generic, once with Qualia-1 context. Watch the context card — each fact lights up at the exact moment it changes the agent's behavior.
Qualia-1 sits between any agent and its context — most useful wherever agents work repeatedly inside a live workflow.
Match how much a copilot helps to how the session is going — stepping back when the user is in flow, stepping in when they stall.
Give agents human-context alongside task, tools, and memory — so they respond to the interaction, not just the request.
Let IDE agents read the state of a coding session — deep focus, debugging, or going in circles — and adapt the next step.
Help AI features tell whether a user is progressing, correcting, or about to abandon a flow — and respond before they drop off.
See interactions from the user's side — beyond prompts, outputs, latency, and cost — to understand what actually happened.
One structured input. It slots in front of the agent without changing the model behind it.
Talk to us →Two kinds of evidence: public agent benchmarks re-run with Qualia-1 context in our internal harness, and product metrics from simulated pilot sessions. Baselines are real, cited, and current as of June 2026. Qualia-1 figures are ours — read them as a research preview, not audited results.
We're working with selected teams building AI copilots, agents, developer tools, and workflow products. Qualia-1 is an SDK and API for teams who want to add user-side context to their AI product.
Teams building:
Design partners who can help validate: