What we can do for you today.
Two ways to put the geometric tech to work right now. Engagements with our R&D team, or licensing the patents that sit underneath everything we ship.
Trying to build a more symbiotic relationship between humanity, AI, and the environment.
Engagements in our R&D team.
Engagements run out of IAMAI Labs, MMV's research and development department. The same five people building the technology. No middlemen, no farmed-out work.
Save. Replace expensive retraining cycles with continual learning. Compress the knowledge models train from at 1,000–2,900× while preserving 88–99% of full-data classifier accuracy.
Make. Ship on devices the cloud-only stack couldn't reach. Phones, laptops, vehicles, edge boxes. Open new revenue lines where the cost-per-inference used to kill the unit economics.
Advance. Continual learning that improves your model in production. No retraining downtime. Selective unlearning that meets GDPR / EU AI Act data-deletion requirements. Two patented geometric foundations your competitors can't license anywhere else.
Architecture review
A second pair of eyes on your AI stack.
We read your model architecture, identify the parts wasting compute, and deliver a one-pass critique with concrete fixes. Two-week engagement.
Custom integration
Continuous Learning bolted onto your existing model.
Drop our continual-learning layer onto a frozen base model. The model keeps learning, with selective unlearning for compliance. No retraining cycle. We pair on the integration; you keep the model.
R&D pilots
A short engagement to test compression on your data.
Six to twelve weeks. We compress your embedding caches with our Knowledge Compression engine, score the retention on your downstream tasks, and write the results up clean so your team can decide what to do next.
MENTI
Continual learning for any model.
MENTI is our continual-learning product, built on the patented Continuous Learning architecture (IAMAI002). Drop it on top of any large language model (open or closed, frozen or not) and the model keeps learning from new data without retraining. It also supports selective unlearning to meet GDPR and EU AI Act data-deletion requirements. Validated on Permuted MNIST across five sequential tasks with no measurable forgetting, plus multi-domain LoRA on Qwen 0.5B. A 7B-parameter run is in flight. For enterprise teams that don't want to retrain from scratch every time the world changes.
VIVERE
The shape, not the data.
VIVERE is our compression engine, built on the patented Knowledge Compression architecture (IAMAI001). It compresses the embedding caches AI models train and run on at 1,000–2,900× while preserving 88–99% of full-data classifier accuracy. On Banking77, a 77-class intent benchmark, VIVERE beats the FAISS PQ M=2 industry baseline by +4.5 accuracy points and is 3.3× smaller at the same time. License the encoder for use inside your own stack.
The patents underneath, available to license.
The geometric IP held by IAMAI Lab Holdings, LLC. Available for integration into existing platforms and stacks.
Knowledge Compression
Compress what models learn from.
License the patented geometric compression layer. Compresses caches of embedding vectors at 1,000–2,900× while preserving 88–99% of full-data classifier accuracy. Patent IAMAI001, held by IAMAI Lab Holdings, LLC. Per-deployment or hyperscaler tier.
Continuous Learning
Update deployed models without retraining.
License the continual-learning architecture. Models absorb new domains without forgetting prior ones, and selectively forget specific data on demand. This meets GDPR / EU AI Act data-deletion requirements. Patent IAMAI002, held by IAMAI Lab Holdings, LLC.
Combined-stack licensing
Both technologies, one agreement.
For partners who want the full stack: compression on the training side, continuous learning on the deployment side. Same target business model as ARM, Qualcomm, Dolby. A foundational-efficiency layer paid by everyone who builds on top.