Evaluate in systems
A model that aces a benchmark can still fail with tools, memory, or a changed environment. We test how components behave together — where errors compound and what breaks first.
How we work — not what we build.
The technology page lists our capabilities. This page is about the standards, methods, and questions that guide all of them. We measure progress by what systems do in context — not by press releases or isolated benchmarks.
Why breadth
We research across modalities because AGI requires all of them.
A system that only understands text can't drive. One that only sees can't reason about plans. One that predicts but can't act can't do anything useful. General intelligence isn't a bigger language model — it's the integration of perception, language, memory, emotion, analysis, and action into something that learns as a whole.
That doesn't mean we scatter effort randomly. It means each project is chosen because it closes a specific gap — and because what we learn in one area transfers to others.
Vision & object ID→ perception for driving, agents, and simulation
Speech & emotional AI→ human-facing interfaces and context-aware reasoning
LLMs & text systems→ knowledge, planning language, and tool orchestration
Predictive analytics→ forecasting layers inside agents and autonomy stacks
Autonomous agents→ the execution layer True AI will need to act on the world
Simulate before deploying
Synthetic worlds for driving, visual feedback, and agent behavior. Failure should be cheap, repeatable, and informative — not discovered in production.
Publish the gaps
We'd rather know where we're weak than inflate where we're strong. Honest measurement drives the roadmap. Capability without accountability isn't progress.
Open questions
Problems we're actively working through.
Cross-modal memory
How does a system retain what it learned from text, images, and actions — and apply that knowledge in a new modality entirely?
Transfer learning at scale
When does solving one problem make the next one easier — and when are we just training separate models and calling it integration?
Reliable agency
How do you build agents and autonomous systems that recover gracefully, know their limits, and stay auditable when they fail?
Affect without theater
Can emotional intelligence be measured and evaluated rigorously — not as performance, but as genuine context understanding?
Working on adjacent problems?
If your research touches general intelligence, multimodal learning, evaluation, or safety — we'd like to compare notes.
Reach out