Rilev is building the baseline layer for human reliability.
Organizations measure servers, revenue, supply chains, and users in real time. They still manage the human system with surveys, lagging claims, and guesswork. Rilev turns psychological assessment into private, structured, actionable data.
23
validated scales
150+
pattern models
Zero-knowledge
architecture by design
Based on validated instruments developed at:
The Problem
The largest operating expense in business is still the least instrumented.
Mental health and human performance are now board-level issues, but the tools remain shallow: annual surveys, EAP utilization, disability claims, exit interviews, and wellness content.
Those systems tell leaders that something already broke. They do not create a private baseline, detect earlier patterns, or explain what kind of support is actually needed.
Rilev is designed to become the missing measurement layer between people, clinicians, professionals, and organizations.
Rilev gives human systems the same baseline discipline software teams expect from telemetry.
A massive category is moving from benefits spend to measurable infrastructure.
Employers are already spending. Individuals are already searching. Clinicians and professionals need better intake data. The gap is not demand; it is trusted measurement.
$18.2B
Corporate wellness market
Large budget category with weak measurement
76%
Employees report symptoms
Demand is already inside the workforce
$300B+
Annual employer cost
Untreated mental health becomes operational drag
One assessment. Three compounding assets.
Rilev is not a wellness content app. It is a structured data company built around assessment, interpretation, and private deployment.
Clinical Signal Layer
Rilev digitizes 23 validated psychological scales into one structured assessment, mapping 68 outcomes across symptoms, personality, relationships, work, and meaning.
Pattern Intelligence
A cross-dimensional engine detects 150+ compound patterns that individual questionnaires miss, turning fragmented scores into interpretable human signals.
Privacy-Native Data Model
Identity, payment, and assessment data are separated by design, giving users and organizations a credible path to honest psychological data.
B2B distribution with a consumer data advantage.
Organizations
Anonymous workforce baselining, aggregate trend analytics, and team-level risk visibility for employers that need earlier signals without creating surveillance.
View applicationsIndividuals
A personal assessment experience that creates demand, improves the report engine, and gives Rilev direct access to high-intent psychological data.
Try assessmentDefensibility
The moat is the combination, not any single feature.
Rilev sits at the intersection of privacy architecture, clinical measurement, and AI-native interpretation. That combination is difficult to retrofit once trust has already been lost.
Privacy moat
The architecture is not a compliance afterthought. It is the trust layer that makes sensitive psychological measurement usable at scale.
Clinical moat
Rilev combines validated instruments, scoring logic, outcome mapping, and interpretation across domains that are usually measured in silos.
AI moat
Structured psychological data powers report generation, pattern synthesis, and enterprise analytics in a format generic wellness tools cannot easily reproduce.
Built, shipped, and ready for serious conversations.
Rilev is beyond the concept stage. The product, data model, reporting system, and professional surface are already operating.
23
Validated scales
Unified into one assessment
68
Outcomes mapped
Per completed profile
150+
Pattern models
Cross-dimensional intelligence
2
Live products
rilev.com and pro.rilev.com
Production system
Next.js, Firebase, Cloud Functions, Vercel edge, assessment scoring, report generation, and professional sharing are live as a working product.
Enterprise surface
Rilev Pro supports shared reports, practitioner workflows, organizational deployment, and aggregate insight paths for pilots.
Report engine
AI synthesis turns structured assessment outputs into readable, personalized profiles in minutes instead of manual interpretation cycles.
Founder Behind Rilev
Keramat Saeedi
Trained in Physical Chemistry, Keramat left doctoral research to build. He went on to scale a regulated cryptocurrency exchange with full KYC/AML compliance and launch multi-channel e-commerce operations from the ground up.
Today, he brings that same infrastructure-first mindset to psychology—building a zero-knowledge architecture for true anonymity and making human systems readable and reliable across individuals, teams, and enterprises.
ConnectRilev is building the infrastructure layer for psychological data.
We are interested in investors, accelerators, enterprise pilots, clinical partners, and operators who understand that the next generation of mental health infrastructure must be private, measurable, and system-level from day one.