AI-powered research matchmaking
for STEM education
Omovera built an AI system that accurately matched high school and undergraduate students with professors and PhD interns on real STEM research projects across the US and UK, while managing the entire journey from inquiry to paid engagement.
Business & academic outcomes
The system wasn’t just a recommendation engine—it became the core operating layer for a research-driven EdTech platform.
High-quality matches
Deep alignment between student interests, skill levels, and mentor research areas led to strong engagement and completion rates.
Scalable operations
Manual academic matching was replaced with AI-assisted triage and recommendations, enabling scale without quality loss.
Revenue enablement
The agentic workflow converted qualified leads into paid research programs with minimal manual intervention.
Before vs after
Before
- • Manual academic matching by counselors
- • Inconsistent quality across mentors and projects
- • High turnaround time for student placement
- • Fragmented lead-to-enrollment journey
After
- • AI-driven, evidence-based matchmaking
- • Consistent academic rigor across STEM domains
- • Faster student-to-project onboarding
- • Fully orchestrated lead-to-paid workflow
What we built
A domain-aware, agentic AI system designed for high-stakes academic matchmaking.
Student profiling intelligence
- • Academic background & coursework depth
- • Subject interests within STEM
- • Research readiness & learning goals
Mentor & project understanding
- • Professor research focus & constraints
- • PhD intern availability & mentoring style
- • Project complexity and prerequisites
AI matchmaking engine
- • Multi-factor semantic matching
- • Confidence scoring and ranking
- • Human review for edge cases
Agentic workflow orchestration
- • Lead intake & qualification
- • Match confirmation & scheduling
- • Program onboarding & payment flow
Technical depth (selective)
Designed to handle nuanced academic language, prerequisites, and expectations.
Semantic understanding
Deep parsing of research descriptions, abstracts, student essays, and mentor requirements across STEM domains.
Confidence-driven routing
High-confidence matches auto-approved; low-confidence cases routed for human academic review.
Building AI for complex human matching?
We design agentic AI systems for high-stakes domains—where accuracy, trust, and outcomes matter.
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