Ojas · AI engine
Ojas is our own AI engine. It runs the reasoning and agent workflows inside our products, and the same pipelines power the systems we build for clients — on their infrastructure, against their data.
Agent orchestration
Multi-step agents with tool use, state, and hard limits on what each step is allowed to do.
Private retrieval
Retrieval over corpora that never leave the customer boundary, with per-identity access control.
Evaluation & guardrails
Every pipeline ships with an eval set. Behaviour is measured before release, not described.
Observability
Traces for every run, so a wrong answer can be explained rather than guessed at.
Brain-like models that learn without backpropagating the whole network.
Ojas treats training and serving as one system. Updates are local and gated rather than global, so a model can keep learning while it answers, and the work can be split across ordinary machines instead of a single large cluster.
Gated local updates
A surprise gate decides which steps are worth learning from — 96% of quality at 25% of the updates.
Serve and learn together
Activations stay resident from the forward pass, so learning needs no second pass over the model.
Split across machines
A model is cut into spans that run on separate nodes and stay token-identical to a single-machine run.
Runs on what you have
Apple silicon, NVIDIA, CPU and mobile GPUs run the same kernels, checked against a CPU oracle.
Want Ojas pointed at your own data and workflows?
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