A Behavioral Approach: The Pattern is the Gray
Patent-Pending • 1500+ Behavioral Dimensions • Frictionless Verification
Traditional authentication proves who logged in, then stops watching. But identity integrity needs to persist beyond the login moment—continuously verifying that the same entity remains in control.
Chat room account takeovers, session hijacking after login, mid-conversation compromise. Someone logs in as you, but are they still you five minutes later? Ten minutes? An hour?
Constant interruptions for re-verification break workflow and frustrate users. Imagine if your system just knew it was you—continuously, frictionlessly, without asking.
Did someone hijack my AI agent? Is this still the same autonomous system I deployed? Current identity systems weren't built to validate non-human entities continuously.
Imagine if the systems you interact with knew it was you—simply by how you interact with them. Your interactions are incredibly unique to you. The way you type, pause, decide, and respond creates a behavioral fingerprint that's distinctly yours. MyndPrint captures these patterns across thousands of dimensions and generates a cognitive signature. When you interact again, we derive a new signature, compare it to your established pattern, and verify your identity—continuously, silently, without interrupting your flow.
How you interact, not what you know. Your cognitive patterns can't be stolen, forgotten, or shared like passwords and tokens.
Always watching, never interrupting. Verification doesn't stop at login—it persists throughout your entire session.
Works for humans and AI agents alike. The same technology validates both biological and autonomous entities.
MyndPrint establishes behavioral continuity across systems. Instead of classifying who or what an entity is, it verifies whether each interaction originates from the same cognitive source. This behavioral identity layer enables persistent reauthentication, drift detection, and provenance tracking across both human and AI-driven ecosystems — without friction, tokens, or stored biometrics.
Continuously verifies user or agent identity in the background through behavioral consistency rather than static credentials. Ideal for replacing traditional MFA with frictionless, continuous verification that scales across devices and platforms.
Detects when the behavioral signature of an authenticated entity begins to diverge — signaling possible session hijack, account compromise, or cognitive drift in AI agents. MyndPrint quantifies the when and how of identity change in real time.
Confirms whether two communications, logins, or interactions originate from the same behavioral source — without exposing private data or content. Used to link identity across systems, verify authorship, and maintain continuity across sessions or networks.
Integrates with existing SSO or IAM frameworks to provide an ongoing behavioral "trust signal." When risk increases or drift is detected, MyndPrint dynamically adjusts authentication policy or triggers revalidation.
Ensures that AI agents operating across workflows or environments can be reidentified and validated by their unique behavioral signature — proving consistency, not classification. Useful for verifying cloned, updated, or federated models in multi-agent systems.
Acts as a secure, API-driven behavioral record layer that can timestamp, verify, and reconcile identity signals across applications. Creates a non-repudiable audit trail of who (or what) acted — without storing PII or raw content.
MyndPrint establishes an invisible behavioral layer between user and system — validating control, intent, and continuity with every interaction.
Every keystroke, phrasing choice, and timing pattern reinforces a behavioral trust signal — no prompts, no friction.
The system learns and adjusts to natural variation while flagging true anomalies that indicate risk or drift.
Delivered as lightweight middleware, MyndPrint connects to existing IAM, SSO, or API gateways — enriching them with continuous behavioral context.
MyndPrint transforms natural interaction data — text, dialogue, or system inputs — into a multidimensional behavioral fingerprint. Each signal passes through 13 specialized extractors measuring lexical choice, syntax, rhythm, emotional tone, and interaction cadence. These metrics form a 1,564-dimensional signature vector that's normalized in L2 space to ensure fair, comparable distance between users and sessions.
The result is a persistent cognitive signature that remains stable over time yet sensitive enough to detect drift — whether caused by impersonation, delegation, or AI substitution. Our pipeline merges behavioral science, machine learning, and cryptographic integrity to create a verifiable identity layer that operates silently beneath every interaction.
Character patterns, punctuation, lexical choices, reaction times, decision patterns, cognitive rhythms
Demonstrated 100% match accuracy under controlled MPv5 test conditions (threshold 0.25)
Specialized extractors analyze distinct behavioral patterns across cognitive and interaction styles
MyndPrint.AI is a behavioral identity intelligence company building the trust layer for the AI era. We verify who or what is behind every digital interaction — continuously, silently, and without friction.
Our patent-pending system captures over 1,500 behavioral dimensions across 13 specialized extractors to form a unique cognitive signature for each human or AI agent. This enables persistent reauthentication — verifying identity not just at login, but throughout every session and interaction.
At MyndPrint.AI, we believe identity shouldn't be a one-time event. It should persist — adapting as behavior changes while maintaining a continuous link to the authentic entity in control.
Trust is no longer about what you know (passwords) or what you have (devices). It's about how you think, respond, and behave — patterns that can't be faked, cloned, or borrowed.
Our mission is to make behavioral identity the foundation of trust between humans and machines.
MyndPrint.AI is in the early demonstration and validation phase, having successfully completed internal proof-of-concept testing through the MPv5 cognitive signature pipeline. This prototype achieved 100% match accuracy across human and AI-generated datasets, demonstrating the ability to detect behavioral consistency, drift, and impersonation.
We're now focused on:
MyndPrint.AI is seeking strategic investors and technology partners to accelerate commercialization and scale. We're particularly interested in working with:
Together, we can establish a new standard of trust — replacing static checks with continuous behavioral verification.
Pete Fitzgerald is a technologist and builder focused on AI enablement and the architecture of intelligent systems. With decades of experience bridging software, networks, and automation, Pete has dedicated his career to helping organizations operationalize AI — turning abstract intelligence into measurable, scalable outcomes.
MyndPrint.AI was born from a single question Pete asked an AI during development:
"How do you know it's me?"
That moment — part curiosity, part challenge — became the spark for building a system where trust is continuous, not occasional. MyndPrint.AI embodies his belief that identity isn't a checkpoint; it's a living signal that should flow through every interaction between humans and machines.
A hands-on engineer and forward thinker, Pete blends deep technical fluency with a maker's curiosity — designing, building, and deploying solutions that bring trust, autonomy, and intelligence into harmony.
As AI systems gain autonomy and digital interactions blur between human and machine, identity persistence becomes critical to security, accountability, and trust. MyndPrint.AI ensures that identity remains personal, provable, and persistent — even when the entity on the other end isn't human.
MyndPrint.AI — From factors to flow. Identity that persists.