Solutions by Domain

Intelligence for the physical world

Ulysses brings real-time sensory AI to the domains where it matters most. No GPUs. No retraining. On the hardware you already own.

How It Works

Raw Reality
Sensor data, signals,
streaming time-series
Ulysses
Energy-Based Model engine
Real-time sensory intelligence
Your AI Stack
LLMs, ML pipelines,
domain models

Ulysses doesn't replace your AI — it gives your AI eyes. It sits upstream of your existing stack and feeds real-time intelligence to LLMs, ML pipelines, and domain models. Think of it as the sensory front-end for everything you've already built.

Capital Markets & Financial Services

Markets don't wait for batch processing. Ulysses detects regime shifts in streaming financial data in real time — using Energy-Based Models that run on the Xeon infrastructure your firm already owns. No GPU procurement delays. No retraining. Just intelligence at the speed of markets.

COVID regime shift detected with 100% recall — compared to 26% with standard approaches. Comparable to what quant titans like Renaissance, Two Sigma, and Jane Street achieve, but with 34x fewer parameters on CPU.
Regime Detection Anomaly Detection Real-Time Risk Monitoring Signal Processing
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100%
Regime Recall
34x
Param Efficiency
<10ns
Inference

Defense & National Security

In contested environments, intelligence can't depend on cloud connectivity or GPU availability. Ulysses processes RF signals and streaming sensor data at the edge — on the Xeon hardware already deployed in theater. Zero GPU dependency means lower SWaP and real-time threat assessment where it matters.

ITAR-compliant architecture co-designed with Intel Labs. Platform integration through CACI. Runs on existing deployed Xeon infrastructure with zero GPU dependency and minimal SWaP footprint.
RF Signal Processing Edge Intelligence Anomaly Detection Real-Time Threat Assessment
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Zero GPU required
ITAR compliant
Low SWaP profile
Edge-native deploy

Healthcare & Life Sciences

Patient monitoring generates massive volumes of streaming sensor data — vitals, waveforms, device telemetry — most of which is never analyzed in real time. Ulysses sits upstream of clinical ML models as a sensory front-end, processing physical-world data continuously and surfacing anomalies before they become events.

Real-time processing of streaming vitals and waveform data on hospital edge infrastructure. Deploys as a sensory pre-processor upstream of existing clinical AI systems — no GPU infrastructure required.
Real-Time Patient Monitoring Waveform Analysis Clinical Anomaly Detection Medical Device Intelligence
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72
bpm
Heart Rate
98
%
SpO2
16
/min
Resp Rate

Cross-cutting capabilities

Every vertical benefits from the same foundational architecture. These capabilities span all deployments.

Real-Time Anomaly Detection

Continuous monitoring of streaming data with sub-10ns detection latency. Energy-Based Models identify deviations from learned patterns without explicit anomaly labels.

Streaming Signal Processing

Process high-frequency time-series data in continuous streams. No batching, no windowing artifacts. Native support for multi-dimensional sensor feeds.

Edge Deployment

Full inference capability on edge hardware without cloud dependency. Designed for disconnected, intermittent, and low-bandwidth environments.

Zero-Copy Inference

Direct memory access inference pipeline eliminates data serialization overhead. No copying between CPU and accelerator — because there is no accelerator.

CPU-Native (No GPU)

Optimized for Intel Xeon with AMX instructions. Zero GPU dependency means deployment on existing infrastructure with no procurement cycle and no specialized hardware.

Upstream Integration Layer

Sits in front of LLMs, ML pipelines, and domain-specific models as a sensory pre-processor. Enriches downstream AI with real-time physical-world context.

What does this look like in your domain?

Every deployment is different. Let's explore how Ulysses maps to your data, your infrastructure, and your mission.

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