Unleash the power of
Local AI
Frontier-grade reasoning on your own GPUs. RLM (Recursive Language Models) reads the whole problem, not a summary of it. The Revyzor plug-in’s compression makes that affordable, and signs every step. Your data never leaves your estate.
Enterprise AI is stalling on cost and control
The bill keeps growing, and nobody can govern what the AI is doing. Local inference on open models fixes both. No per-token API bill that scales with success. No data crossing your perimeter. Models are commoditising; the advantage now sits in how you run them.
Open models on your own GPUs. Fixed, predictable costs, the opposite of the runaway spend killing AI pilots today.
No third-party endpoint, no cross-border transfer, no vendor retention. The data, the model and the reasoning stay inside your estate.
Every reasoning step kept and cryptographically signed. Governance built into the layer the AI runs on, not a policy document bolted on afterwards.
Two components. One local stack.
RLM is the engine that reads everything. Revyzor is the plug-in that makes it affordable and keeps the evidence.
RLM (Recursive Language Models)
- Reads the whole case, document set or history, recursing into sub-questions instead of truncating or summarising.
- Runs on local open models that now approach frontier quality.
- Every sub-call is logged: which question, which evidence, which answer.
- Pluggable analysis profiles per workload, from financial-crime review to claims and clinical documentation.
Revyzor Compression
- Proprietary, hardware-accelerated compression of the model’s working state, entirely on-GPU.
- Lossless compression at 1.444x on bf16, with the roundtrip bit-identical and the output identical word for word.
- Lossless mode preserves the state exactly: byte-for-byte checked at capture.
- Every state signed (W3C credentials) and persisted. Reload a 5,097-token state in 56.2 ms, against 417 ms to re-read the document.
- Free a third of the cache memory on every GPU, so the hardware you already own carries longer contexts or more concurrent users.
Deep recursion produces thousands of reasoning states. Compressing them losslessly is what keeps the storage bill for holding, and signing, every step in proportion. That is the unlock: full-power local reasoning with the evidence kept.
Deploys where you already are
One plug-in, built against the ecosystem’s standard interfaces. No model changes, no retraining, no application changes: a single configuration flag.
The dominant open inference engine. 100% match against the base model on LongBench v2; full needle recall at 128K context.
Validated end-to-end: full accuracy parity with the uncompressed baseline.
Validated on H100 inside NIM. Rides NVIDIA's enterprise distribution into regulated estates.
Drops into ByteDance's pluggable compression slot; 12–22% smaller than the compressor it ships with.
What Local AI can now do
The workloads that were blocked, too sensitive for a cloud API, too deep for a single model call, too consequential to run without a record, are exactly where RLM + Revyzor runs.
Financial-crime review
Read the entire case history, thread typologies across periods, and land every flag on a named reviewer with the decision signed and on the record.
Claims & underwriting
Whole-file analysis of claims and policy documents on your own infrastructure, with an inspectable record of what the model relied on.
Clinical & regulated docs
Deep reading of documentation that can never leave the perimeter, with every reasoning step kept and cryptographically signed.
AI adoption isn’t failing on capability.
It’s failing on cost and control.
Local delivers both. RLM is the reasoning engine; the Revyzor plug-in is what makes local affordable, governable and defensible.
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