Quantum General Intelligence provides a deterministic AI stack built on quantum embeddings for regulated decision‑making in credit, claims, compliance, and clinical settings.
Funding
Funding not disclosed
Founders
Product
Problem
Regulated decision workflows in credit, claims, compliance, and clinical settings require explanations, auditability, and reproducibility, but existing probabilistic AI models produce nondeterministic outputs that cannot be reliably defended to regulators or auditors.
Solution
Quantum General Intelligence (QGI) offers a deterministic AI stack built on quantum‑structured embeddings that encode enterprise data, rules, and dependencies into a hypergraph. The stack’s five layers—Q‑Prime embeddings, the QAG Engine, Qualtron generation, Quantum Graph Memory, and Neural Symbolic Agents—process inputs through a single reasoning graph, producing decisions that are replayable, signable, and defensible. By surfacing seven explicit Hilbert‑Space Compacting signals (relevance, conflict, overlap, redundancy, coverage, coherence, topology), the system highlights rule conflicts before generation, ensuring outputs align with the actual policy rather than statistical guesses. The stack is delivered via Enterprise Blueprints that integrate customer data and approval workflows, enabling regulated teams to deploy decision‑grade AI without custom engineering. An initial application, Q6, demonstrates the approach as an EQ‑first, safety‑aware model powering the Uniti Q wellness companion for U.S. veterans.
Target Audience
Primary customers are regulated enterprises in financial services (mortgage, credit, claims), insurance, healthcare, and government agencies that need auditable AI decisions, as well as technology partners building compliant workflow solutions.
Features
- Q‑Prime quantum‑structured embedding model that encodes polarity, scope, conditions, and cross‑rule dependencies into a hypergraph, running on standard GPU hardware
- QAG Engine reasoning layer that projects high‑dimensional states into seven interpretable signals to surface conflicts before generation
- Qualtron composite generation with a 4‑million token context, replacing generic LLMs for domain‑specific precision
- Quantum Graph Memory (QGM) provides time‑aware, provenance‑preserving graph storage for facts, decisions, and revisions
- Neural Symbolic Agents runtime orchestrates multi‑agent workflows, enforcing deterministic execution and audit trails
- Enterprise Blueprints package the full stack into ready‑to‑launch regulated workflows with rule and data integration
- Replayable, signable decision objects that combine output, explanation, and evidence for regulator or auditor review
- Q6 model adds emotional‑intelligence handling, time‑aware context across sessions, and safety boundaries for wellness applications