Kavara
Try the endpoint
GA · All major clouds · CPU-native

Inference. Without Training.

Find patterns in raw, unlabeled data — on the CPU you already have, at a price that makes sense.

Try the endpoint Read the docs No GPU. No labels. No retraining.

Raw stream in → patterns out · continuous, no retraining

The shift

Stop labeling. Stop renting GPUs. Stop retraining.

The label-then-train-then-retrain ritual was built for stationary data on borrowed GPUs. Real streams aren't stationary, and the GPUs aren't there.

The old way

Label → Train on GPU → Deploy → Retrain

  • — Labeling project that never finishes on streaming data
  • — GPU procurement: scarce, expensive, often unavailable at the edge
  • — Training cycle measured in weeks; the data drifts in hours
  • — Retrain on schedule and hope; live with model rot in between
  • — Can't deploy to SCIF, vehicle, sensor pod, or air-gapped site
Inference Without Training
The new way

Point Ulysses at the raw stream

  • → No labels. Raw data is the input.
  • → No GPU. Runs on the CPU you already have.
  • → No retraining. Every sample updates the model in place.
  • → Real-time embeddings and predictions, continuously adapted.
  • → Deploys anywhere there's CPU — cloud, edge, on-prem, air-gapped.

The Kavara Inference Platform

Model as a Service.
The Ulysses Models, callable on CPU.

Ulysses is an Active Online Inference engine — a family of online learning energy-based models that continuously adapt to non-stationary, noisy time series through entropy-driven updates, delivering real-time embeddings and predictions with zero retraining.

Reach the Ulysses Models over a single REST endpoint or as an MCP-discoverable tool. One inference call per sample. Per-call pricing. No setup, no GPU plumbing, no labeling pipeline upstream.

Service

Model as a Service
Inference as a Service

Surface

REST API
MCP tool

Generate API key View MCP schema
api.kavara.ai/v1/ulysses/infer 
# Point Ulysses at a raw stream. One call per sample.
# No labels, no training step, no GPU.

curl https://api.kavara.ai/v1/ulysses/infer \
  -H "Authorization: Bearer $KAVARA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ulysses-stream-1",
    "sample": [0.421, 0.418, 0.430, 0.425, ...],
    "return": ["embedding", "scalar", "reconstruction"]
  }'

# → embedding[]  : real-time vector for downstream agents
# → scalar       : anomaly score, no threshold tuning required
# → reconstruction: what "normal" looks like right now
Sub-10ns
per-sample inner loop
CPU-native
x86 · ARM · any cloud
Per-call
priced for agent routing

For the routing function

Agents are the ultimate rational actors.

A human can be sold a worse-but-prettier vendor. An agent can't. Agents route to the endpoint with the best capability per dollar per millisecond. Below is what the routing function reads.

Cost per call

$0.0008

Standard tier · CPU spot

p50 latency

3.1 ms

N=200 sample · same-region

p99 latency

9.4 ms

95th percentile under load

CPU footprint

1 core

Cache-resident inner loop

MCP-ready

Yes

Discoverable as a tool

Retraining

None

Adapts inline, every sample

Benchmark figures are representative for `ulysses-stream-1` on Intel Sapphire Rapids spot capacity at the time of publication. See the docs for SKU-by-SKU latency curves and pricing tiers.

Industry-agnostic

Wherever streams move faster than retraining cycles.

Ulysses is built for any domain where the data is non-stationary, the failure modes aren't in the label set, and the compute is whatever's already in the rack.

Capital markets
Signal intelligence
Industrial telemetry
Climate & atmosphere
Network operations
Healthcare monitoring
IoT & sensor fleets
Defense & intelligence
Agentic systems

About Kavara

To braid.

Kavara means "to braid" in Sanskrit. Kavara, Inc. is the company behind the Kavara Inference Platform and the Ulysses Models.

Kavara provides Artificial Intelligence as a Service (AIAAS) services featuring software using artificial intelligence (AI) for development and deployment of quantum mechanical systems. The Kavara Inference Platform delivers the Ulysses Models as a Service (MaaS) — accessible through a REST API and as MCP-discoverable tools in the agent-to-agent ecosystem.

Ulysses is an Active Online Inference engine — a family of online learning energy-based models that continuously adapt to non-stationary, noisy time series through entropy-driven updates, delivering real-time embeddings and predictions with zero retraining. We braid quantum mechanics with classical mechanics using canonical-ensemble Boltzmann math: Hermitian operators, eigendecomposition, density matrices, and von Neumann entropy form the quantum strand; Boltzmann weights and canonical-ensemble probability form the classical strand. The braid is where they meet — and the meeting point is what produces the regime-aware signal the practitioner consumes.

Because Ulysses' model operations fit in CPU shared memory, the inner loop runs at full CPU clock speed (up to 5 GHz on modern x86, where GPUs are clock-locked around 1 GHz). That's why Kavara runs anywhere the data lives: any CPU vendor, any cloud, on-prem or off, air-gapped or connected. The algorithm follows the data — not the other way around.

Built for the data scientist and ML engineer chasing unknown unknowns: capital markets, signal intelligence, industrial telemetry, climate, network operations, healthcare, IoT — anywhere regimes shift and the next surprise isn't in the training set.

Inference Without Training.

Find patterns in raw, unlabeled data — on the CPU you already have, at a price that makes sense.

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