Applied AI research lab

Models that learn in milliseconds and forget exactly.

Our first product, APM (Adaptive Plasticity Module), updates a model in about two milliseconds. The result is identical to retraining from scratch.

Adaptive Plasticity Module · live
Classes the model knows
2.2 msto update
0.0difference vs full retrain
Try it. Each change applies instantly.
Illustration using timings measured on an NVIDIA A100.
~2 msper exact update
557×faster than retraining, same GPU
10−13gap to a full retrain
0.4 msper prediction
The problem

Every change to a trained model means waiting for a retrain.

New tool, new intent, a deletion request, one wrong answer. Today each one means retraining, fine-tuning that forgets, or a filter that hides the problem.

Time to apply one change · same NVIDIA A100
Retrain the modelstandard approach
1,238 ms
APMAdaptive Plasticity Module
2.2 ms
Same data, same GPU, same accuracy (95.5% vs 95.7%)On a 2-core CPU: ~300 ms vs 71 s
01 · Learn

New knowledge, live instantly.

Add a tool, product or category with a few examples. No training run, no downtime.

02 · Forget

Deletes you can prove.

Remove data and the model is identical to one that never saw it, not just filtered.

03 · Correct

Fix one answer, touch nothing else.

Corrections changed only 0.16% of other predictions in our tests.

04 · Undo

Every change is reversible.

Roll back a bad batch and get the previous model back exactly.

Explore the Adaptive Plasticity Module →
Results

Measured on real workloads, on the same hardware.

96.5%

right tool in the top 5 on a 1,000-tool agent benchmark, versus 86.3% for description search

10,000

random adds and deletes, and the model still equals a fresh retrain

~1 pt

from conventional training accuracy, across 8 languages

See all benchmarks →
Use cases

For systems that change every day.

Pilots open

See what the Adaptive Plasticity Module does on your data.