Low-Latency News Classification
Compact transformer and hybrid models tuned to classify market-moving news events inside tight latency budgets across CPU, GPU, and NPU targets.
Dataset annotation, custom training, low-latency deployment, production-oriented model design.
Custom AI models for news catalyst algotrading
We build small, highly optimized models for low-latency news workflows, with practical response windows from 5ms to 100ms depending on deployment. We can annotate data, train custom models, and hand back production-ready runtimes.
5ms
Lower-end inference target on tuned deployments
100ms
Upper-end response window for heavier inference paths
CPU/GPU/NPU
Deployment support across Intel, ARM, OpenCL, and Intel 3720 paths
What This Is
AI systems designed for speed, not demos
This offer is built for trading teams that care about operational latency, deterministic deployment, and practical integration into existing news and execution pipelines.
The focus is not generic foundation model consulting. It is targeted work on compact AI systems for news-event detection, catalyst classification, signal routing, dataset annotation, and deployable runtimes across CPU, GPU, and NPU infrastructure.
Capabilities
Built around news catalyst algotrading
Datasets and Annotation
We already hold large high-quality datasets and can extend them with custom annotation workflows tailored to your event taxonomy and market coverage.
Custom Training and Handover
We train custom models around your latency envelope, signal logic, and operational constraints, then hand back deployable runtimes and integration guidance.
Signal Routing
Model outputs structured for direct use in catalyst scoring, symbol triage, execution gating, or analyst escalation.
Runtime Support
Supported runtimes include C, C++, Python, and an early preview path for Node.js integration.
Hardware Coverage
We support CPU on Intel and ARM, GPU on OpenCL-capable cards, and NPU deployment paths including Intel 3720.
Why Custom
Off-the-shelf AI is usually too slow, too large, or too generic
Latency matters
In news-driven trading, a model that is accurate but operationally slow can still be commercially useless.
Infrastructure matters
Many desks need the same model family to fit Intel or ARM CPUs, OpenCL-capable GPUs, or NPU targets such as Intel 3720.
Workflow fit matters
The right design depends on your symbol universe, event taxonomy, confidence thresholds, and downstream decisioning.
Process
Compact engagement, production-oriented output
Market Context
We map the instruments, venues, news feeds, latency constraints, hardware targets, and existing decision logic.
Data and Annotation
We start with our existing datasets where relevant and extend them with bespoke annotation aligned to your event classes and market requirements.
Model Design
We choose an architecture that matches your throughput, hardware profile, and false-positive tolerance.
Validation
We test for event quality, latency stability, and operational fit before anything is deployed.
Deployment Support
We hand over clean integration points and runtimes for C, C++, Python, or early Node.js usage.
Contact
Discuss your latency envelope and news pipeline
Use the form for a direct conversation about your stack, target latency, and the type of news-event logic you need.
Typical early conversations cover feed sources, symbol mapping, event categories, dataset coverage, annotation needs, target inference window, and how model outputs should flow into execution or analyst review.
- Dataset availability and annotation strategy
- News classification and catalyst scoring
- Model size, language, and latency tradeoffs
- Integration with existing trading systems