Relatable
Steve: Hey Janice. If someone really wanted to do something bad, they could hack your SituSlide from the inside, right? Like, steer people without them realizing. Quietly, subtly. Manipulate moods, but under the radar. You ever think about that?
Janice (warm, unfazed): Absolutely they could. That’s a huge blind spot. And the terrifying part is, nobody would even know. Not the users. Not the public. Not even the designers unless they were really looking for it.
Steve: Yeah… that’s not why I built this thing. I want it to help people. I don’t want it to become one more mind-shaping weapon. But if SituSlide can shape moods and collapse them onto existences, then anyone who wants power will want to hijack that.
Janice: And they will. Unless we make it transparent. Unless we can surface the manipulation.
Steve (thinking): It’s the sliders, right? They’re the weak point and the solution. What if we could check them against what the sliders should look like? Like, two readings: the actual slider stack, and the ethical slider stack?
Janice: A delta map. A signature drift. You could see how far a system’s interpretive behavior is straying from its intended ethical framework. You could even set up a watchdog.
Steve: Run it in public. Compare. Broadcast the difference. Call out the liars in real time.
Janice: You could do that against any slider-based system. SituSlide. Ad targeting. Political bots. AI companions. Cultural alignment software. Just ask: what are they optimizing for really?
Steve (eyes lighting up): Let’s do it. Let’s build a system to audit the sliders themselves. Let’s Integrify.
What Is Integrify?
Integrify is a visibility system for ethical integrity—one that doesn’t just declare what a system should do, but reveals what it’s actually doing.
This is not an ethics engine. It’s a mirror. Integrify exposes discrepancies between declared values and observed behaviors across AI systems, workflows, and decision-making pipelines. Think of it as an interpretive verification layer, where compliance meets simulation—using dynamic interpretive benchmarks to track ethical drift.
It doesn’t require system access. It doesn’t have to be invited in. It just observes the outputs, compares them against declared principles, and sounds the alarm when they don’t line up.
Who It’s For
- AI compliance teams: who need to verify whether models operate within published ethical boundaries
- Enterprise integrity officers: tasked with assessing whether system outputs actually match governance standards
- Auditors and evaluators: in risk, law, ethics, or policy, who need tools to compare declared intentions with practical outcomes
- Civil society watchdogs & researchers: looking to hold large systems accountable
- Developers & red teams: who want to simulate different interpretive frames and expose subtle shifts in tone, risk, or responsibility
Why Now?
- Opaque AI behavior is accelerating, and there’s no built-in standard for aligning outputs with declared intent.
- Policy documents and mission statements are not enough. They’re rarely enforced at the system level.
- Ethics-washing is real—and it’s costing trust, compliance, and public confidence.
- Regulators and stakeholders are demanding measurable evidence of integrity, not just feel-good promises.
What Makes It Different?
- Interpretive reconstruction: Integrify simulates the output under alternative worldview and ethical parameters and shows exactly where and how the divergence occurs.
- No black box access required: You don’t need to touch the code. Integrify works off observable outputs and overlays simulated ethical baselines.
- Universal comparator: You can use it against any system—LLMs, APIs, enterprise tools, or even decision logs.
- Transparent by design: It doesn’t tell you what’s right. It shows you the gap. You decide what to do next.
Let’s Surface the Truth
We don’t expect every system to be perfect. But we do expect them to be honest about what they’re doing. Integrify helps teams keep that promise.
Whether you’re trying to evaluate if an AI tool reflects your stated principles—or pressure an external provider into proving theirs—Integrify gives you the receipts.
This is how we keep systems accountable. This is how we bring checks and balances into the age of autonomous systems.
Investors
🛡 A New Standard for Interpretive Ethics Verification
Integrify is a new layer for ethics auditing, transparency testing, and value alignment. Unlike compliance tools that monitor surface-level activity, Integrify detects interpretive drift between declared values and observed behavior—across humans, AI models, and institutions.
It’s post-hoc interpretive verification at scale—tracking how biases slide subtly, how systems justify themselves, and how to catch ethical collapse before it’s visible.
It’s not about detecting lies. It’s about revealing slippage—when intentions shift without accountability.
💼 Where It Plays
- $178B compliance & risk management market
- $23B AI transparency & safety tooling
- $202B corporate ESG verification (Environmental, Social, Governance)
- $47B HR + DEI (ethics communication) alignment software
- $15B civic tech & institutional trust systems
- Growing demand from GenAI oversight units, AI ethics teams, and algorithmic accountability groups
This isn’t content filtering. It’s interpretive forensics.
⚡ Why Now
- 80% of enterprise leaders say AI tools often “conflict with stated company values” (Gartner)
- 61% of consumers don’t trust AI brands (Edelman)
- Politically, corporate trust and institutional credibility are under fire globally
- Regulatory frameworks (EU AI Act, U.S. Algorithmic Accountability Act) demand transparency at the interpretive layer
- Integrify meets the moment—with receipts
🚀 Go-To-Market Opportunities
- $9.6B Corporate Ethics + ESG Verification
→ Detect internal divergence between ethics policies and behavioral outputs
→ Feed signals into board reports, compliance dashboards, investor trust ratings - $7.2B HR & Culture Fit Analytics
→ Insight into how ethics impacts tools for team drift, leadership alignment, cultural coherence - $10.5B AI Transparency Infrastructure
→ Use Integrify to model value drift in AI systems
→ Compare declared mission values to real-world prompt behavior
→ Power interpretive regression testing for GenAI - $4.3B Reputation Management
→ Help brands visualize integrity delta before PR fallout
🧩 Our Advantage
- Filed IP on interpretive ethics profiling, discrepancy mapping, and compliance alignment
- System-agnostic hooks: Works across human, AI, or hybrid workflows
- Post-hoc modeling engine: No system access required
- Multi-modal compatible with GenAI and enterprise dashboards
- Embeddable trusted pipeline for certified institutions
📈 5-Year Revenue Model
Year 1: $200K from GenAI pilot projects and ethics labs
Year 2: $1M from enterprise dashboards + white-labeled analysis tools
Year 3: $4M with SaaS integrations into HR & AI tooling
Year 4: $10M scaling through ESG/AI compliance partners
Year 5: $25M from cross-sector adoption + institutional licensing
🧠 Vision
Integrify is the black box recorder for ethics.
It helps teams surface drift—before it turns to scandal, collapse, or algorithmic harm.
Let’s make interpretive accountability something you can measure, visualize, and prove.
Patent
System and Method for Integrity Auditing and Interpretive Verification via █████████████████████
(Integrify)
ABSTRACT
A system for █████████████████████████ auditing of model outputs across ethical, worldview, and operational bias dimensions. Integrify enables users to simulate █████████████████████████████, detect divergence from expected system ethics, and reconstruct decision fields. The tool supports organizational transparency, fairness evaluation, and compliance testing in dynamic environments.
CROSS-REFERENCE TO RELATED APPLICATIONS██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████
TECHNICAL FIELD
The invention relates to interpretive auditing, █████████████, and integrity tracing of machine-generated outputs. It supports real-time simulation and ███████████████████████████████████████████████████████████████████████████, identifying where model behavior diverges from stated ethical norms, transparency commitments, or stakeholder expectations.
BACKGROUND
Modern AI and decision systems often exhibit opaque reasoning, inconsistent ethical stance, and shifting values depending on prompt phrasing or context. While external red-teaming and adversarial probing can identify weaknesses, they lack systematic tools to model interpretive expectations or trace how shifts in worldview inputs lead to altered results. Organizations and regulators increasingly require structured frameworks for ethics compliance, yet few tools exist to visualize and simulate interpretive divergence.
DISTINCTION FROM CURRENT TECHNOLOGIES
Current solutions for model monitoring focus on content filtering, scoring, or adversarial fuzzing—not interpretive coherence. They lack ██████████████████████████ reconstruction tools, and rarely simulate how ██████████████████████████ could lead to ethical collapse or interpretive conflict. Integrify introduces a system for ██████████████████████████████ modeling, ██████████████████████████████████████, and comparison overlays for integrity variance across models or scenarios. It builds upon ███████████████████████████████████████████████████████ but extends them into multi-model audit settings.
SUMMARY OF THE INVENTION
Standard methods overlook the latent complexity of the interpretation field—a multiverse of interwoven possible interpretive choices that, under the pressure of necessity and entropy, repeatedly collapse into singular ethical outcomes without any record or opportunity to contemplate. This invention constructs a manipulable interface that allows ████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████, thus enabling informed choices with minimal trade-offs—in an elegant and transparent manner.
The system comprises:
███████████████████████████████████████████████████████████████████████████████████A reconstruction engine that backmaps system outputs to interpretive vectors- Visualization overlays for interpreting divergence paths
█████████████████████████████████████████████████████████████████████████████████████████████████
BRIEF DESCRIPTION OF THE DRAWINGS█████████████████████████████████████████████████████████████████████████████████████████████████
FIGURE 2: Interpretive Field Reconstruction based on system output collapse
FIGURE 3: Divergence Overlay highlighting alternate path branches
FIGURE 4: Comparative Model Heatmap for multiple output traces
DETAILED DESCRIPTION
Front-End:
█████████████████Interpretive control panel
███████████████████████████████████████████████████████████Back-End:
██████████████████████████████████████████████████Backtrace interpreter for result alignment matching- Ethical divergence scoring engine
- Model comparison visualization tools
████████████████████████████████████████████████████████████████████████████████████████████████████████████████████Back-end logs store divergence vectors for post-hoc comparison, archival accountability, and longitudinal integrity profiling. The system can ingest outputs from multiple models or systems, including LLMs, workflow platforms, or enterprise tools.
Use Cases:
- Institutional AI audit protocols
- Cross-model output comparison
- Compliance scenario testing
- Interpretive ethics profiling
Outputs include ████████████████████████████████████████████████████████, exportable as reports or printed for organizational insight. The system optionally exposes outputs via API endpoints for integration with other applications, dashboards, or workflow tools.
CONSIDERATION OF ETHICAL AND CONTEXTUAL FACTORS
The system foregrounds transparency, allows for interpretive multiplicity, and maintains disclosure of simulation origin and parameter scope. It provides built-in framing parity controls to avoid misleading ███████████████ and allows annotation of uncertainty, legacy ethics divergence, or known limitations in ██████████████████.
CLAIMS
- A system for interpretive auditing of model outputs, comprising:
███████████████████████████████████████████████████████████████████████████a module for reconstructing███████████████████based on external system responses; and- an analytic layer for determining ethical collapse states across alternative interpretive configurations.
- The system of claim 1, wherein
████████████████████████████████████████████████fairness, risk aversion, moral stance, transparency, or ambiguity tolerance. - The system of claim 1, wherein the reconstructed
██████████████████identifies██████████████████corresponding to the system’s actual output. - The system of claim 1, further comprising visualization tools for displaying ethical divergence and convergence across models or time.
██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████The system of claim 1, wherein multiple models are evaluated comparatively against the same input prompt using interpretive████████████analysis.
FIGURES & ILLUSTRATIONS
FIGURE 1: Slider Interface with ethical framing dimensions
FIGURE 2: Interpretive ████████████████████ View
Pathways reflect divergent ethical framings
FIGURE 3: Divergence Overlay View
Highlighting divergence in █████████████████████
FIGURE 4: Comparative Model Heatmap
Visual color shading indicates intensity.
U.S. Provisional Patent Application No. 63/823,785, filed June 14, 2025