Q1 - Q2 2025
Phase 1: AI Engine & Core Buildout
Develop the foundational AI engine, data infrastructure, and core psychometric/aptitude models.
- AI Pathfinding Engine MVP
- Google Cloud Infrastructure Setup
- Initial Model Training
- Secure API Layer
Product Roadmap
Our strategic roadmap for bringing MENTIS to life, from the core AI engine to a full-scale commercial launch.
Product Development Phases
Q3 - Q4 2025
Phase 2: Platform MVP & Data Integration
Build the user-facing platform, integrate initial labor market data, and prepare for internal alpha testing.
- User Authentication & Dashboards
- Job Portal Data Integration
- Skill-Gap Analysis MVP
- Internal Alpha Launch
Q1 - Q2 2026
Phase 3: Institutional Pilots & Beta Launch
Deploy MENTIS in partner universities, refine institutional dashboards, and launch a public beta.
- Institutional Dashboards
- University Pilot Programs
- Quizzera.PK Integration
- Public Beta Launch
Q3 2026 onwards
Phase 4: Commercialization & Scale-Up
Roll out paid B2C and B2B subscription models and begin scaling user acquisition and institutional partnerships.
- Launch of Paid Plans
- Onboarding B2B Clients
- Mentisera Academy Integration
- Marketing Campaigns
MVP Readiness & Execution Plan
A transparent, realistic overview of our current progress, risks, and strategic milestones.
MVP Implementation Status
~30%
Implemented in code
~60-65%
Complete at design, logic, and system definition level
All core components are intentionally partial to enable calibration with live data post-launch, ensuring the model adapts to real-world user interactions.
MVP Readiness
Aligned status of core components.
| Component | Status |
|---|---|
| Psychometric framework | Partially defined |
| Scoring & logic models | Partially defined |
| Career–skill taxonomy | Partially mapped |
| User journeys (UX) | Core flows defined |
| Frontend / Backend | Early build |
| AI recommendations | Post-core MVP |
| Institutional dashboards | Phase-2 |
Execution Plan & Milestones (Next 6 Months)
Top 3 Risks & Mitigations
| Risk | Level | Mitigation |
|---|---|---|
| Model calibration risk (requires live data) | Medium | Human-in-the-loop validation strategy |
| Engineering bandwidth risk | Medium | Tightly scope-protected MVP; focus on core flow |
| Institutional pilot pacing | Low-Medium | Parallel B2C rollout to ensure continuous user feedback and data collection |
Hard-Failure Milestone
Failure to launch a live MVP where users can complete the full core flow (assessment → profile → career match → roadmap), even if only in calibration mode. This is our single point of accountability.