Neuronwise Labs.
Diagnostic Triage Agent
Reduced patient onboarding time by 68% using Nucleus Edge with securely containerized LLMs.
Company News.
Case studies, client testimonials, and company updates.
Case Studies.
Fraud Pattern Recognition
Nucleus Retrieval analyzes vector similarities in transaction data at 10,000 TPS with full audit trail.
Predictive Maintenance
Anomaly detection across acoustic sensors on manufacturing lines, powered by Nucleus Orchestrator.
What Clients Say.
"Nucleus Edge reduced our inference latency by 45% while keeping all data within our VPC. No other product offered that guarantee."
"The Orchestrator gave us predictable, verifiable outcomes in scenarios where every other framework hallucinated."
"Nucleus Retrieval's citation chains gave our compliance team the confidence to approve autonomous orchestration in production."
Led by engineering.
Nucleus AI was founded by distributed systems engineers who grew tired of the disconnect between academic ML research and operational reality. We build products that run exactly as defined.
Julian Cross
Founder & CEO
Frequently Asked Questions.
How long does product deployment take?
Standard Nucleus deployments complete within 4 weeks from system audit to active production loops.
How do your products handle data privacy?
All Nucleus Edge inference nodes run entirely within your VPC. No proprietary data ever leaves your network.
Do your products lock us into specific LLM providers?
No. The Nucleus Orchestrator is model-agnostic, supporting standard OpenAI-compatible schemas and local vLLM endpoints.
Can we review the agent logic?
Yes. All Nucleus workflows use open standards like LangGraph so your engineers can inspect and modify nodes directly.
What happens if an agent fails?
Nucleus Orchestrator triggers immediate fallback to human operators or alternative legacy logic trees.
What level of support do you provide post-deployment?
All products include 24/7 SLA-backed telemetry monitoring and priority incident response.
How is pricing structured?
Pricing is based on compute node count rather than per-token, offering predictable scaling costs for every Nucleus product.