Synapse Neural Intelligence — The Integrity Layer for AI

Making AI Trustworthy at Scale

SNI is a multi-model AI orchestration architecture that solves AI's three existential threats: synthetic data collapse, hallucinations, and energy waste — with production-validated results.

94%
Hallucination Reduction
Production validated
80%
Energy Savings
Spectral routing efficiency
<0.1ms
Routing Latency
Query classification
0
Synthetic Contamination
55-day production run

SNI Architecture — 8-Layer Integrity Stack

Click any layer to explore. SNI sits across AI models as a vendor-agnostic orchestration layer.

L7
Edge / Robotics / IoEPhysical instantiation — SNI Brain activation at 95%+ confidence
Extends SNI from cloud to physical systems. The SNI Brain (Frontal Policy → Synapse Router → Sensor Fusion → Motor Planner → Actuator Control) governs robotics and IoE devices. Physical systems activate only when clean-data confidence exceeds 95% and both CLH supervisors and Guardian approve. Includes SubC Prism for underwater environments.
L6
Master Control PlaneDeterministic, vendor-agnostic multi-cloud governance
Built in Rust for deterministic performance. Governs policy versioning, hardware kill-switches, and multi-cloud routing (GCP primary, AWS secondary, Azure tertiary). The MCP is rules-based, not AI-driven — every decision is auditable and reproducible. No single cloud provider dependency.
L5
Sentinel Digital TwinText-blind behavioral drift detection per model
Sentinel cannot read text. It monitors behavioral patterns only: token flow rates, response timing, entropy signatures (Shannon entropy H(t)), phase parameters (Δθ). This makes it immune to prompt injection and social engineering. Each AI model gets a dedicated twin in an isolated sandbox. Drift detection in <1 second vs. the 45-minute industry average.
L4
Guardian Immune SystemEnvironment monitoring across 8 security domains
Monitors 8 domains: model integrity, data provenance, network security, supply chain, regulatory compliance, adversarial detection, performance degradation, energy anomalies. Escalation states: NORMAL → WATCH → INCIDENT → LOCKDOWN. Includes hardware kill-switch capability and foreign signature detection.
L3
Collaborative Learning HubCross-model consensus with rotating supervisors
The primary mechanism behind the 94% hallucination reduction. Multiple AI models cross-validate outputs. Rotating supervisors (SME-certified) prevent single-point bias. No output advances without consensus certification. This is distributed consensus applied to AI — the same mathematical foundation as Byzantine fault tolerance.
L2
Prism Spectral RouterROY-G-BIV routing with Chameleon adaptive security
Maps query complexity to spectral bands (Red=general → Violet=emergency). Classification in <0.1ms. Up to 1,000 sub-channels per band. Simple queries never touch high-compute models — this drives the 80% energy savings. Chameleon tri-state security: Separated (normal), Combined (active threat — models pool for defense), Ghost (sovereign-only, invisible to external).
L1
Data SlicingTenant-isolated partitioning by domain & sensitivity
Partitions data by semantic domain (healthcare, finance, defense), sensitivity class (public → classified), and regulatory framework (HIPAA, SOC2, ITAR, GDPR). Models access slices on-demand — they never process full datasets in monolithic context windows. Cross-slice access requires Guardian authorization.
L0
@Birth LabelingImmutable 8-component provenance tag at data creation
Every data element receives an immutable 8-component label at creation: Origin, Data Type, Policy, Token Class, Wavelength Coordinate (λ), Coupling Sequence, Key ID, Slice ID. The topology hash (SHA-256 of all 8) is the coupling mechanism — there is no single key to steal. An attacker must reconstruct the entire topology simultaneously. Built in Rust.

🔬 SNI Query Router — UI Simulation (Illustrative)

See how Prism classifies and routes a query through the integrity stack

Note: This UI simulation is for readability. Production routing, policy enforcement, and validation run in the SNI control plane.

Q
Your Query
Natural language input
λ
@Birth + Prism
Label → Classify → Route
CLH Consensus
Cross-model validation
Sentinel Check
Behavioral verification
Verified Output
Integrity-assured

Ecosystem

NVIDIA
Inception Program
Google
for Startups Cloud
AWS
Activate Program

About Fabric Plexus

Fabric Plexus builds the integrity infrastructure for AI. Our Synapse Neural Intelligence architecture sits across AI models as the trust layer — validating outputs, preventing synthetic contamination, and providing the audit infrastructure that regulated industries require.

SNI does not compete with AI models. It makes all of them trustworthy. The same way financial markets need independent auditors, AI needs an independent integrity layer.

  • Founded: 2024
  • Core Engine: Rust — deterministic, memory-safe
  • Cloud: GCP (primary), AWS, Azure
  • Models: Agnostic — Claude, Gemini, GPT, Llama, Mistral
  • Validation: Live Palantir deployment (Jul–Oct 2025, 42 nodes)
  • Team: Vishal Ahluwalia (CEO), Giselle Santos (CPO), Aman Jawsal (CTO)
  • Advisors:
  • JC Herz — Senior Commercial Advisor, DARPA (WRC); Advisor to DARPA Commercial Strategy Office. Focus: technology transition and commercialization of breakthrough research. Former SVP, Cyber Supply Chain at Exiger (Ion Channel acquisition). Fellow, National Security Institute; Former co-chair, NTIA Software Transparency Standards; Former advisor to DARPA Defense Sciences Office.
  • Natalie Lehr-Lopez — Executive Director, Supply Chain and Counterintelligence Risk Management (SCRM) Task Force, National Counterintelligence and Security Center (NCSC), Office of the Director of National Intelligence (ODNI). Oversees federal ICTS supply chain security programs and implementation of the Federal Acquisition Supply Chain Security Act. Adjunct Lecturer, Georgetown University (Securing Digital Supply Chain). 20+ years in national intelligence, specializing in cybersecurity, insider threat, and supply chain risk management. M.A., International Relations, Yale University.
Intellectual Property: USPTO Provisional Patent Application 63/887,002. Non-Provisional pending. Sole inventor: Giselle Santos. Architecture spans 8-layer integrity stack with novel @Birth labeling, spectral routing, and topology-based coupling mechanisms.

Founder

Giselle Santos — Founder & Chief Product Officer

Technologist with 20+ years building critical infrastructure across financial services, IoT, supply chain risk, and AI. Background includes Nokia Bell Labs/Nuage, BT (GBFM Innovation Lead), Honeywell Aerospace, Exiger, and Asia Netcom/Pacnet.

Advanced studies in theoretical physics under Dr. Michio Kaku (CCNY). Two decades of cross-domain pattern recognition led to identifying and solving AI's synthetic data collapse problem before industry recognition.

  • Nokia Bell Labs / Nuage — MultiCloud, Multi-Tenant, Native Cloud, Microservices, Wireless
  • BT — Client Partner & Innovation Lead, GBFM
  • Honeywell Aerospace — Maritime IoT, compliance automation
  • Exiger — Supply chain risk, AI governance
  • Asia Netcom / Pacnet — Undersea cable infrastructure
  • Palantir Beta Program — Jul–Oct 2025 (55 days, 42 nodes)
  • Clients: JPMC, Santander, Nomura, Wells Fargo, Maersk, JetBlue
“The industry is optimizing neurons. The constraint is the synapse. A physics-governed control plane unlocks capability without continuous model replacement.”