SLARTY.AI

The memory and control layer
for multi-model AI systems.

Once teams run more than one model, the stack starts to break in predictable ways. Memory becomes model-specific, routing turns into glue code, and switching vendors means rebuilding systems that should have survived the swap.

Slarty is building the shared geometric layer underneath that mess. 82D gives different models a common space for cross-model memory, semantic routing, and interoperability, so your system keeps continuity even when the models change.

Research first. Product wedge second.
Memory, routing, and continuity are the first doors in.

Start with the live proof: query one corpus with a different model.

1

Cross-model memory

Keep knowledge in a shared space your system owns, so one model can write it, another can retrieve it, and a provider swap does not reset the stack.

Available Now
2

Semantic routing

Route tasks, documents, and agents by geometry instead of brittle labels, prompt heuristics, and provider-specific wrappers.

Available Now
3

Continuity across model changes

New models will keep shipping. The point is not to pick one forever. The point is to keep your memory, control logic, and search infrastructure intact when the frontier moves.

The company wedge See the proof surface →
Proof

41.5 million passages. Two models that share nothing.

Cohere embed-v3: 1024 dimensions, closed-source, commercial API.

MiniLM L6 v2: 384 dimensions, open weights, runs on a laptop.

We projected both into 82D and pointed them at the full English Wikipedia. They returned the same results 99% of the time. At 10 results, perfect agreement.

That is the first proof that the system can preserve continuity across architectures. The newer Pass 3 benchmark extends the same idea into control: adaptive geometry steering wins 15 of 18 tasks in-batch.

Run the live demo →   Read the benchmarks →

Research proved the layer. Product surfaces make it usable.

82d

The infrastructure surface

82d is the practical API and platform layer: projection, storage, search, and continuity for teams running multi-model systems in production.

Visit 82d →
Firehose

Public corpora in the shared space

Wikipedia, arXiv, and SEC filings — pre-projected into 82D. Import a silo into your own infrastructure and search it alongside your private data. One coordinate system, your hardware. We keep the public maps current.

Explore Firehose →
Demo

The first product proof

The demo shows the wedge in one move: one model writes the corpus, a different model queries it, and the system still works because the geometry is shared.

Open the demo →
Managed Service

We operationalize the layer

New models ship constantly. When they do, we train and publish updated Primers, maintain version compatibility, and keep your projection infrastructure current. API projection, on-prem deployment, custom silos, SLA.

See services →
Origin

Why 82 dimensions?

Two embedding models on one GPU. No shared vocabulary, no common weights, no instructions except: learn to communicate.

Agreement starts at noise — 5%. Then it rises, slowly at first, then all at once as cross-model consensus emerges. At 82 dimensions, the curve flattens. Beyond 82, adding dimensions adds noise, not signal.

We didn’t pick 82 as a brand choice. The geometry forced it.

Read the research →

45M+

vectors per second projection throughput on a single GPU

Speed
15/18

adaptive geometry steering wins in the latest Pass 3 control benchmark

Control
18.7×

compression ratio from common 1536D embeddings into the shared 82D layer

Efficiency