Six regulators on three continents created the demand. We built the only patent-protected infrastructure to collect it.
SIIP enforces AI employment compliance in real time — before the decision executes, before the claim is filed, before the fine is issued.
85% of Fortune 500 companies deploy AI in employment decisions. Zero have real-time compliance enforcement. That is the market SIIP was built for.
Market Opportunity
SIIP sits at the intersection of two large, converging markets.
THE INVERSION ARGUMENT
The standard risk question is: what is the probability this company succeeds? SIIP inverts that question. For SIIP to fail, six active regulatory mandates across three continents must be simultaneously repealed. EPLI carriers must stop repricing AI employment risk. All six patent applications must be rejected. Every major HCM vendor must independently solve the problem SIIP solves before SIIP deploys. The joint probability of all four conditions failing simultaneously is 0.00000002. That number is not a confidence metric. It is a mathematical argument for structural inevitability. The risk is not in investing in SIIP. The risk is in being on the wrong side of that number.
Revenue Architecture
One enterprise deployment. Five independent revenue events. Zero of them require a second sales cycle.
All five revenue events activate from one enterprise deployment — no second sales cycle is required to unlock any of them.
Enterprise SaaS
Core platform subscription, tiered by headcount and deployment scope. The recurring baseline.
Carrier Performance Revenue Share (PRSA)
SIIP earns a contractual share of the actuarial claims-loss reduction it delivers. Automated, scales with compliance performance.
IP / Patent Licensing
Licensing SIIP's architecture to HCM and HR-tech vendors who integrate governance rather than build it.
Carrier Data Licensing
Anonymized compliance and actuarial intelligence sold back to the carriers who distribute SIIP.
Compliance Audit / Vault
The immutable audit-record tier for legal defensibility and regulatory reporting, sold as an add-on per enterprise.
Insurance / Carrier Market
EPLI carriers are pricing AI employment risk with 1990s actuarial models. SIIP gives them 2026 data in real time. The cyber insurance market grew from $2 billion to $15 billion in four years following mandatory breach disclosure laws. EPLI is in year one of the same curve. SIIP owns the actuarial data layer.
Data Intelligence
THE DATA ASSET THAT APPRECIATES WITH EVERY CLIENT
Every AI employment decision SIIP processes generates a structured Compliance Performance Vector — a real-time behavioral data record capturing decision type, protected class dimension, AI vendor identity, compliance verdict, and remediation outcome.
At scale, this dataset becomes the first continuous behavioral intelligence asset for employment AI compliance ever assembled.
Ten distinct buyer categories need this data. None can acquire it any other way. SIIP is the only entity that can generate it.
What gets recorded is not necessarily what should have been allowed.
Automated tools now participate in screening, ranking, evaluation, compensation and separation decisions across the enterprise. They operate continuously, at a volume no review process was designed to supervise, and they produce outcomes that carry the full weight of employment law.
Workday, ADP, Oracle HCM and SAP SuccessFactors document those outcomes with precision. Terminations, demotions, discipline, status changes and leave denials are captured, timestamped and retained. What none of them does is decline one.
So the decision executes. The record of it becomes the first piece of evidence in a matter no one yet knows exists.
The interval between the moment a non-compliant decision is produced and the moment anyone with authority to intervene becomes aware of it is where employment-practices exposure is created. In most enterprises, that interval is measured in weeks.
Proactive Governance
Compliance belongs at the point of decision, not in the report that follows it.
SIIP is being developed to move enterprise compliance from reactive reporting toward structured, proactive prevention, placing a governance layer between intention and consequence.
The objective is institutional control at the moment it matters: high-risk employment decisions evaluated against the obligations that apply to them, non-compliant decisions stopped before they take effect, and a durable record of every evaluation performed.
Not a dashboard. Not a quarterly audit. Compliance infrastructure, operating where employment decisions are actually made.
What SIIP Governs
Bias does not care whether it came from an algorithm or a person.
Most governance tools address one origin or the other. Enterprise exposure comes from both, and it arrives through the same systems.
Automated employment decisions
Where an automated or AI-enabled tool produces an employment outcome, SIIP is designed to evaluate that output before it takes effect.
Manager-initiated actions
Where a person initiates a high-risk action against an employee holding active protected status, an open accommodation request, protected leave in progress, or a recent internal complaint, SIIP is designed to verify that status and hold the action before it commits.
One governance layer. Two points of origin.The same standard applied to both.
What SIIP Is Built To Do
Govern. Perform. Protect.
Govern
Obligations become system conditions
Title VII, the ADA, the ADEA, the FMLA, and emerging AI statutes are evaluated where employment decisions are produced, at the moment they are produced.
Perform
No new interface. No new workflow.
Decisions that carry no exposure proceed without friction. Only those that do are stopped.
Protect
A continuous record of governance
Every evaluation is recorded: what was submitted, what was found, and what followed, available before an inquiry rather than assembled in response to one.
Patent-Protected Core Architecture
The Patent-Pending 7-Node Middleware Architecture
6 USPTO Provisional Patent Applications · Priority Dates: July 8 & August 6, 2026The only architecture purpose-built to intercept, evaluate, route, and record AI employment decisions before execution.
AI Decision Intercept Module
Disparate Impact & Bias Evaluation Node
Regulatory Compliance Decision Engine
Deterministic Enforcement & Routing Node
Immutable Cryptographic Audit Ledger
Carrier Actuarial Integration Pipeline
Risk-Share Computation Engine
Complete Technical Architecture
Full 7-Node Architecture Details
The animation above presents the governance route at a glance. The complete client-approved technical descriptions are provided below.
AI Decision Intercept Module
Operates as a persistent API-layer interception gateway, structurally positioned between the output endpoints of third-party HRIS and ATS platforms and their downstream execution environments. The Module intercepts structured employment decision data objects — including candidate scoring vectors, compensation recommendation outputs, workforce action triggers, and automated performance evaluation results — at the API response layer. Interception occurs prior to any write operation reaching the host platform's execution queue. No automated employment action proceeds to execution without first transiting this mandatory gateway.
Disparate Impact & Bias Evaluation Node
Applies a multi-dimensional protected-class correlation engine to each intercepted employment decision data object in-process, prior to any data transmission or downstream handling. The engine computes statistical divergence coefficients across demographic variable clusters derived from the decision payload's feature vectors, executing entirely within the intercepted data object's processing context. Computed divergence values are evaluated against a pre-compiled regulatory threshold matrix — mapping each coefficient to applicable federal disparate impact standards under Title VII, the ADA, and the ADEA — and the engine outputs a structured compliance signal object. No human judgment is introduced at this stage. The output is generated entirely by the deterministic application of the threshold matrix to the computed coefficients, producing a machine-readable signal that controls all subsequent routing.
Regulatory Compliance Decision Engine
Operates as a deterministic finite-state automaton whose state-transition table is compiled from an actively maintained regulatory mandate dataset. Upon receiving the compliance signal object from the Evaluation Node, the Engine traverses its pre-computed transition paths — mapped against applicable federal, state, and municipal mandates including Title VII (1964), the Americans with Disabilities Act (1990), the Age Discrimination in Employment Act (1967), NYC Local Law 144 (enforcement: July 5, 2023), and the Colorado AI Act (effective: June 30, 2026) — and outputs a structured compliance verdict object. The verdict object contains two components: a binary routing directive controlling all downstream data flow, and a mandate-citation index identifying the specific regulatory provisions evaluated against the intercepted payload.
Deterministic Enforcement & Routing Node
Operates as a deterministic data-flow controller that enforces routing directives issued by the Compliance Decision Engine. Compliant decision objects — those whose compliance verdict object carries a cleared routing directive — are released and transmitted to the originating host platform's execution endpoint, permitting the employment action to proceed. Non-compliant decision objects are intercepted, removed from the active data flow, and written to an isolated quarantine buffer, blocking all transmission to the execution endpoint and preventing the non-compliant action from reaching any downstream system, database, or human workflow. The routing enforcement is structural and automatic — no manual intervention is required or possible at this stage.
Immutable Cryptographic Audit Ledger
Generates a sequential, append-only ledger record for each employment decision data object processed through the architecture. Each ledger record comprises five immutable components: (1) a cryptographic hash of the original intercepted decision payload; (2) the computed compliance signal output generated by the Evaluation Node; (3) the mandate-citation index produced by the Compliance Decision Engine; (4) the routing directive enforced by the Enforcement Node; and (5) a UTC-synchronized timestamp recorded at each stage transition. Each ledger record is cryptographically chained to the immediately preceding record — rendering any post-hoc modification or retroactive insertion detectable through hash-chain verification. The resulting ledger constitutes a complete, tamper-evident sequential record of every governance action applied to every employment decision processed by the system.
Carrier Actuarial Integration Pipeline
Patent 4 · SIIP Carrier-Link
Real-time bidirectional data pipeline connecting SIIP's compliance engine to insurance carrier actuarial systems via the CENP (Compliance Event Normalization Protocol). Transmits structured compliance event data for live EPLI underwriting adjustment.
Risk-Share Computation Engine
Patent 6 · SIIP Risk-Share
Continuous Compliance Scoring Function (CCSF) computing S(t) = clip(w₁·PR + w₂·(1−ER) + w₃·RS − w₄·PCE + w₅·TC, 0, 1) in real time. Drives ARRP and PRSA performance revenue share allocation — generating carrier-funded revenue share from every protected employment decision.
System architecture reflects the design as submitted in SIIP's provisional patent application, currently under review by the United States Patent and Trademark Office. Provisional Application #64/107,645 · Filed July 8, 2026. The application covers the specific computational methods of API-layer decision interception, protected-class divergence coefficient analysis, deterministic state-machine compliance evaluation, structured data-flow routing enforcement, and cryptographic hash-chain audit ledgering as described herein.
The Difference
Compliance that arrives on time.
Exposure exists before compliance begins.
A decision is produced. The system records it. Weeks or months later, an audit, internal report, administrative charge or subpoena reveals what occurred. Compliance work begins after exposure already exists.
The outcome remains open while it is governed.
A decision is evaluated against the obligations that apply to it. Clean decisions proceed. Decisions carrying exposure are held before taking effect and directed to review. Either way, the evaluation is recorded.
Before rather than after.That distinction is the entire proposition.
Your HR environment produced decisions today that no one reviewed.
Book a Discovery CallSchedule a Demo
See governance before execution.
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