Predict which outreach will work before you spend time on it
Mnomis is an outreach intelligence layer that interprets longitudinal behavioral signals, infers engagement structure with a confidence score, and delivers practical guidance inside care workflows.
Infrastructure, not an insight deck
Mnomis operates as a behavioral inference system. It turns raw outreach and care navigation activity into structured signals, interprets them longitudinally, and produces operational guidance with explicit confidence.
Posture is a working hypothesis
Classification reflects persistent orientation inferred from longitudinal patterns. Expression can change with context, so the system updates on a structured cadence and elevates confidence only with sustained evidence.
Confidence tells teams how much to trust it
Every classification includes a quantified confidence score derived from signal quantity, consistency, temporal coverage, and recency. Low confidence cases are treated as learning opportunities.
It improves through outcomes
The model calibrates to your population by learning which signals best predict response by channel, timing, and framing. This creates compounding advantage with deployment.
Built for care workflows
Outputs are delivered as a short brief designed to fit into task views, CRM panels, or care management worklists. The goal is to guide, not automate decisions.
A defined behavioral signal infrastructure
These are examples of structured signals used to infer engagement structure. Signal sets expand during onboarding based on data availability and operational priorities.
Built by experience designers who obsess over what makes people act
Mnomis was created to solve a practical problem: care teams do the right work, but too much outreach fails because the approach does not match how a person receives outreach right now. We build systems that translate real behavior into usable guidance, so teams can spend their time where it matters and engage with more respect.
Human centered, operationally strict
We start with lived experience and behavioral evidence, then encode it into a system that works inside real workflows.
Interpretability is a feature
Care and compliance teams need to understand what is driving guidance. We design for explainability, not mystery.
Measurement first
We treat every recommendation as a hypothesis and every outcome as calibration data. Accuracy improves with use.
What is stable, what changes, and what the system reacts to
People have underlying engagement motivations that tend to be stable over time. What changes is which motivation is most active in a given context. Mnomis separates stable structure from shifting expression, then updates guidance as new evidence arrives.
Stable engagement structure
Longitudinal patterns show how someone tends to relate to guidance, autonomy, reassurance, and continuity. This is what posture inference captures.
Context driven expression
Stress, urgency, friction, and recent experiences can shift responsiveness. The system detects changes through recent signals and adjusts.
Behavior, not beliefs in a vacuum
We do not ask teams to trust psychology. We ground inference in observable events, then make uncertainty explicit with confidence scoring.
Guidance is a hypothesis
The output is a recommended approach, not a label. Outcomes feed calibration so recommendations get better each cycle.
What teams get
Mnomis gives teams a consistent language for readiness, outreach tone, and channel choice, with interpretability that holds up in clinical and compliance review.
Less wasted outreach
Reduce repeated contact that hardens avoidance and focus effort where conditions are most likely to produce engagement.
Higher follow-through
Align channel, timing, and framing to engagement structure so members complete the next step more consistently.
Better handoffs
Provide a brief that explains how the member tends to engage so RNs, CMs, and navigators can tailor the approach.
Posture-informed guidance examples
A simple view of how the system changes outreach approach without changing clinical content.
Best channel pattern
Often responds to information and specificity. Avoid repeated check-ins that feel scripted.
Effective framing
Offer options, emphasize autonomy, provide a clear reason to act now.
Timing guidance
Fewer touches, higher substance. Let the member set pace when possible.
Failure mode
Over-contact converts cooperative members into active disengagement.
Best channel pattern
Consistent response when structure is consistent. Predictable follow-through builds trust.
Effective framing
Clear steps, reassurance, confirm a single next action and remove friction.
Timing guidance
Regular cadence with agreed next contact. A missed visit often needs quick support.
Failure mode
Inconsistent contact looks like abandonment and can appear as avoidance in the data.
Best channel pattern
Responds when context aligns. Channel precision matters less than timing and relevance.
Effective framing
Make the ask feel relevant now. Connect to what the member values. Keep it low effort.
Timing guidance
Concentrate effort around known activation conditions, not high-volume campaigns.
Failure mode
Repeated generic outreach consumes goodwill and reduces future responsiveness.
Best channel pattern
Standard outreach often fails across channels. Consider low-stakes trust building and friction removal.
Effective framing
Short, respectful, and safe. Reduce decision burden. Offer small steps.
Timing guidance
Do less, but better. Avoid repeated unanswered contact that hardens avoidance.
Failure mode
Volume creates noise and can signal that the system is not listening.
Start with a no-integration pilot
Share a small sample export, even 1,000 rows. We return a pilot-ready view, Outreach Intelligence Briefs, and a measurement plan you can review with clinical and compliance teams.
Request a pilot
A simple way to capture interest and route the next step. Replace with your form tool or CRM embed.
Contact
Email: pilot@mnomis.example
Or add your scheduling link here.
What to send
Minimum: member ID, outreach attempts by channel, response events, appointment events, care gap events (if available).
Designed to operate without PII or PHI
Mnomis is intentionally built to work on engagement and interaction patterns, not personal identity or clinical content. Pilots can be executed using pseudonymized member IDs and event data only.
No PII required
We do not need names, addresses, Social Security numbers, or direct identifiers. Use pseudonymized member IDs.
No PHI required
We do not need medical records, diagnoses, clinical notes, or protected health information. Behavioral signals are sufficient.
Purpose limited and deletable
Data is used solely to evaluate engagement and intervention effectiveness. Data can be deleted on request or at pilot completion.
Built for clinical and compliance review
Clear boundaries and interpretability are part of the product.
Is this a nudge or messaging optimization tool? +
No. Mnomis is an inference system that predicts intervention effectiveness by interpreting longitudinal behavioral signals. The output is operational guidance for care teams, not automated persuasion and not a content generation engine.
Does Mnomis replace clinical judgment? +
No. The brief supports a human decision. It is designed to help teams choose tone, channel, and next step while keeping clinical content and protocols unchanged.
How does confidence scoring work? +
Confidence reflects the reliability of the posture inference given the quantity, consistency, temporal coverage, and recency of behavioral evidence. Low confidence results should be treated as provisional and monitored.
What data is required to start? +
The pilot can start with modest history. Outreach logs and appointment signals are often enough for initial inference. Accuracy improves as additional signals and outcomes accumulate.
Why cannot generic AI replicate this quickly? +
The defensibility comes from the structured signal ontology, longitudinal inference logic, outcome-linked calibration, and workflow embedding. Generic AI can summarize or draft text. It cannot replicate a calibrated behavioral inference infrastructure without equivalent operational deployment and outcomes history.