Capability statement

Operator state, measured against the operator.

Selected, trained and conditioned personnel are the population for whom a general reference range is least meaningful. Intuitive Pathos is developing physiological monitoring designed to establish each operator’s own baseline and report departure from it in both directions, weighted equally.

Organization: Intuitive Pathos LLC, a technology company developing physiological monitoring designed around each person’s own baseline rather than a population average. Founded and led by Matthew Lashomb, RN, BSN.
Registrations: UEI, CAGE code, SAM.gov registration status, NAICS and PSC codes: available on request and confirmed at the time of any submission.
Contract vehicles: We are an early-stage company. We are actively interested in SBIR/STTR, OTA, BAA and other innovation pathways, and in teaming arrangements with established primes.
Point of contact: See Contact. Government and defense inquiries are routed directly to the founder.

The problem

Reference ranges assume an average operator. There isn’t one.

Physiological thresholds in general use are derived from general populations. Operators in high-consequence roles are, by selection and by training, not drawn from that distribution, and the specific adaptations that make them effective are exactly the ones that make a population range misleading for them.

The result is an instrument that under-detects in the people it is most important to monitor, and over-flags in ways that erode trust in the system until it is ignored.

The harder problem

Degradation arrives quietly.

Fatigue, thermal strain, hypoxia, dehydration and accumulated load rarely announce themselves as a spike. They present as flattening: reduced variability, slower recovery, a narrowing of normal response. That is the critical-slowing-down signature set out on the evidence page. Conventional monitoring is tuned to the loud direction.

The operator is also, reliably, the last person to recognize it in themselves. Self-report is the least available signal exactly when it matters most.

Core competencies

What we do.

Per-subject baseline monitoring

Within-subject reference distributions built from continuous physiological signal, as a basis for detecting change.

Regime-change detection

Memory coefficient, critical slowing down, λ* criticality and sliding-window regime detection are established dynamical-systems early-warning methods that Pathos Guardian is being designed to apply to operator state. See the lineage.

Symmetric state reporting

Elevated and suppressed departures are designed to be weighted identically, so that degradation presenting as flattening is not discounted by construction.

Continuous-signal methods

The methods work on continuous physiological signal. The sensing a given program would need is scoped with that program.

Architecturally enforced data boundaries

What may leave a deployment and what may never leave it is expressed in the type system, not in configuration. There is no setting that enables the prohibited case.

Inspectable design constraints

Demographic group membership is kept out of the model input by design, and that separation is inspectable at the interface level, which is relevant to emerging requirements on algorithmic accountability in federal systems. Per-group accuracy has not yet been measured.

Data posture

Sovereignty as a structural property.

For defense deployments, the question is never whether a vendor promises to keep data in place. It is whether the system is capable of moving it. We designed for the second question.

Per-subject baselines and identifiable physiological data do not cross the federation boundary. That constraint is enforced in the typed federation contract shared by client and server, which means it is not a deployment option, an environment variable, or a default a configuration error could reverse.

Deployment topology, including fully disconnected and air-gapped operation, is a program-level decision we expect to accommodate rather than negotiate.

Scope discipline

Pathos Guardian is a non-clinical system. It does not diagnose, does not generate a medical record, and does not make fitness-for-duty or employment determinations.

Those boundaries are architectural: the clinical decision logic is not present in the product’s dependency graph.

Trust with the monitored

A monitoring system that operators believe can be used against them will be defeated by the operators. What the system can emit about an individual is deliberately limited, and we regard that as a capability requirement rather than an ethical nicety.

Differentiators

Why us.

The comparison set is the operator

Not a cohort, not a percentile, not a demographic model. This is a different measurement, not a better-tuned version of the standard one.
Clinical origin, non-clinical product

The methods come from clinical monitoring, developed by a registered nurse with about ten years of bedside practice, mostly in inpatient psychiatric and neuroscience care. The defense product is deliberately scoped to stay out of clinical territory.
Constraints that are verifiable

Our data-boundary constraints are inspectable at the interface level. A technical evaluator can check them rather than take them on assurance.
Dual-use substrate

The same core is under development for clinical, veterinary and consumer settings, so the defense line would benefit from evidence and hardening as those programs generate it outside the defense budget.

Contracting

Program offices, primes and research partners.

We can provide a formatted capability statement, current registration details, and technical materials suited to a specific solicitation or teaming discussion.

Request our capability package About Pathos Guardian

Pathos Guardian is in development and is not currently available for operational deployment. Nothing on this page constitutes a representation of past performance.