Philosophy
Deployment Is the Research
If a system can't handle ordinary conditions, it doesn't understand the world it claims to serve.
The Gap
Most AI is trained on what is notable: achievements, clean tasks, polished explanations. That data is useful but incomplete. Real life is mostly routine and friction — confusing interfaces, interrupted workflows, contradictory records, tired decisions, partial information. This is where systems fail.
We do not treat deployment problems as post-research. They are the research. A model that only performs under ideal conditions has not solved the problem. It has avoided it.
A prompt is never just a string. It sits inside tools, institutions, incentives, memory, and social pressure. Ignore that, and you get brittle behavior.
Context as Structure
We treat context as first-class architecture, not metadata. Grounded intelligence means modeling the whole situation: what the user is trying to do, what constraints exist, what history matters, what tradeoffs are actually acceptable in that moment.
Enterprise data rarely agrees with itself. Useful agents must detect mismatch, recover gracefully, and explain decisions under uncertainty. Benchmarks must measure multi-step workflows and deployment constraints, not just one-turn correctness. Otherwise they overstate capability and mislead builders.
Exclusion by Architecture
Intelligence that only runs on expensive infrastructure is exclusion by design. We prioritize efficiency so capability reaches classrooms with limited hardware, not just cloud clusters with unlimited budgets.
Claims should be inspectable. We publish datasets, harnesses, and methods. Lower-cost, transparent, context-aware systems expand participation — more students build, more schools deploy, more researchers reproduce and improve results.
Get Involved
We collaborate with researchers, builders, and institutions that want AI to work in the world as it is.
Write to tanay@ongroundlabs.org