Quantum Ai Innovations

Eigen Agentic SRE

Governed autonomy for production reliability — detect, diagnose, and safely remediate incidents with deterministic guardrails.

Eigen Agentic SRE is a physics-grounded, sovereign SRE agent that validates incident reality before any AI reasoning, isolates real-time evidence from historical recall, and executes only allowlisted safe actions with hard blocks on destructive commands. Deploy on-prem or inside your VPC.

• False-positive pages ↓40–70%

• MTTA ↓25–50%

• MTTR ↓15–40%

• Rollback rate ≤1% (goal: 0)

• Validate reality (noise filtered before reasoning)

• Evidence-first diagnosis (deterministic veto rules)

• Evidence isolation (real-time proof overrides historical similarity)

• Allowlisted execution (safe tools only)

• Immutable postmortems (evidence spans + commands + outputs)

Kubernetes/DevOps: crash loops, OOMKilled, rollout health, scaling
Databases (optional): deadlocks, blocking PIDs, targeted remediation
FinOps (optional): idle capacity, over-provisioning, cost-aware scaling

Read-only first • Copilot mode available (human approval for actions) • Role-based access & scoped credentials • Every action is evidence-linked and fully auditable.

Example: Evidence-linked incident pack with audit bundle (commands + outputs + evidence spans)

Eigen engine - Deterministic Truth for Enterprise AI

Eigen Engine adjudicates conflicting documents using a physics-based truth layer + immutable Sovereign Memory—so your AI answers only from the active, authoritative source or refuses.

• Request a pilot

• Download the white paper

The problem

Semantic similarity can’t tell “Draft v3” from “Final v4.”
Standard RAG retrieves both because they look the same in vector space. The LLM blends them—creating “data rot” hallucinations (wrong pricing, wrong policy, wrong API, wrong clause).

What Eigen Engine does

Eigen Engine converts retrieval into an energy-minimization problem.
Documents “compete” under conflict forces and authority bias until the system collapses to a stable ground state—the single, undisputed truth set.

How it works

1. Vector Probe — pull top candidates by similarity (fast, conventional retrieval)

2. Truth Adjudication — build an energy landscape using:

   • Conflict forces (repel mutually inconsistent docs)

   • Authority field (bias toward FINAL, latest, highest-provenance sources)

3. Eigenstate Output — return only the stable set; if unstable/insufficient → refuse (no guessing)

Key capabilities

Conflict Gate
If high-similarity versions conflict (Draft vs Final, 2023 vs 2025), parameters make coexistence mathematically impossible—only one survives.

Sovereign Memory
An immutable storage layer that enforces hierarchy (FINAL overrides DRAFT) and preserves session continuity as searchable memory particles.

Dual-Brain Mode

• Sovereign Mode: Strict, low-entropy answers from ground state only (or refusal)

• General Mode: For external tasks (emails, writing) when internal truth isn’t required

Audit-Ready
Every suppression and selection has a deterministic reason: which conflict repelled, which authority bias dominated—ideal for compliance.

Use cases

• Sales & RevOps: Suppress expired price lists and old promos → quote accuracy you can defend

• Support: Refer official patch notes over old workaround guides

• Engineering/IT: Prevent obsolete docs from poisoning implementation

• HR: Reconcile legacy vs current policy into one enforceable truth

• Legal/Compliance: Suppress superseded drafts; reduce citation risk

• Finance/Audit: Traceable, provable decision paths

• Pharma/Science: Only current approved protocol surfaces (v50), not v49

Deployment

Real-Time Adjudication for fast-changing ops (Sales, Support)
Pre-Computed Sovereignty (Air-Gap ready) for regulated orgs (Pharma/Defense): nightly ground-state snapshots, zero external dependency

Differentiator

Standard RAG: similarity → mixed versions → probabilistic answers
Eigen Engine: physics adjudication → ground state truth → deterministic answers or refusal

Trust by Design

• Deterministic truth adjudication: For document status & authority (active vs superseded, final vs draft, latest vs outdated).

• Conflict-aware retrieval: Similar documents that disagree cannot co-exist in the final context set.

• Audit-ready evidence: Every suppression/selection is logged with a traceable decision path (conflict signals + authority weighting).

• Refusal-first: When the truth set is unstable, insufficient, or conflicting beyond threshold, the system refuses instead of guessing.

• Role-based reality: Truth can be scoped by team/role with hierarchical controls.

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Maximizing Hardware ROI in Sparse MoE Architectures via Quantum-Inspired Optimization

Q - Distill engine

Cut token costs by 50% without losing facts

• Cost & Speed: Cuts cloud inference costs and latency by ~50% while preserving critical business entities like dates and account numbers.

• Scale: Enables processing of massive logs and datasets that exceed standard token limits.

• Privacy: Automatically filters sensitive PII to ensure strict compliance before data leaves your system.

Q - Consensus engine

Eliminate hallucinations with mathematical consensus

• Truth Verification: Eliminates hallucinations by forcing the AI to verify facts across multiple corroborating sources.

• Analysis: Surfaces distinct, non-repetitive viewpoints for comprehensive research rather than redundant search results.

• Precision: Prevents errors by routing complex queries to the correct specialized agents based on semantic meaning.

Q - Signal engine

Audit your AI for safety risks in milliseconds

• Automated Speed: Replaces manual security reviews with sub-second audits, certifying safety with every deployment.

• Blind Spot Detection: Exposes hidden vulnerabilities by stress-testing models against mathematically diverse attack vectors.

• Risk Mitigation: rigorously tests for privacy leakage and toxicity to ensure GDPR and safety compliance.