Your Matching Process
Runs on Spreadsheets.
How Long Until It Breaks?
95% Faster Medical School Matching: How OpsVoid transformed tribal knowledge into a proprietary AI engine with Stripe-triggered automation.
Spreadsheet Hell
Talearnted Tutors was a victim of its own success. An influx of ambitious medical students created a manual mountain of work—years of matching expertise trapped in rigid spreadsheets that couldn't scale.
The Bottleneck
Matching students to medical schools involved cross-referencing complex criteria against years of proprietary data stored in rigid spreadsheets. Every match was a research project, not a process.
The Cost
Every new lead added a heavy administrative load. The team was trapped in "manual mode"—making infinite scaling impossible and burnout inevitable. Growth meant hiring, not efficiency.
The Risk
Human error in matching could cost a student their future and the agency its reputation. One wrong recommendation could unravel years of trust built with families.
"OpsVoid didn't just automate a task—they built a proprietary intellectual property moat. We transformed years of tribal knowledge into a high-performance technical stack."
The "AI Matchmaker"
Technical Stack
Gemini Pro 1.5 for reasoning. n8n for orchestration. Supabase for vector and tabular storage. Stripe for payment triggers. A full-stack AI agent—not a chatbot wrapper.
Stripe Payment Trigger
The automation begins the moment a student pays. A webhook fires, initiating the entire matching pipeline—no manual intervention, no delays, no leads falling through the cracks.
Data Collection & Sanitization
Years of messy, proprietary matching data had to be structured for machine consumption. We transformed rigid spreadsheets into a clean, queryable knowledge base—preserving the tribal knowledge while making it AI-accessible.
The "Secret Sauce" — Hybrid Search & Filter
Here's where most AI projects fail: they either use pure LLM reasoning (hallucinates) or pure database queries (misses nuance). Our Hybrid Search combines strict SQL filters with LLM-powered reasoning. The AI doesn't just "guess"—it calculates. University requirements are filtered first, then semantic matching ranks the best fits.
AI Recommendation Generation
Gemini Pro 1.5's long-context reasoning produces the 4-5 university recommendations that previously required a human expert's judgment. Each recommendation comes with rationale—not a black box, but explainable AI that the team can trust.
Real-Time Client Dashboard
A custom dashboard lets the client update university criteria live—new programs, changed requirements, seasonal adjustments. The AI stays as smart as their top consultant without engineering intervention.
Before vs. After
Good news on contextual — client has tested and it's working fine too 🙌
— Founder, Talearnted Tutors
The Technical Moat
Proprietary IP Moat
The matching logic isn't just automated—it's owned. Years of tribal knowledge, now a technical asset that competitors can't replicate.
Payment-to-Recommendation Pipeline
The entire lifecycle is one automated flow. No handoffs. No manual steps. Stripe fires, the AI delivers. Revenue to result in seconds.
Real-Time Accuracy
The dashboard lets the client keep the AI current without engineering intervention. The system gets smarter as the business grows.
Scalability by Design
Built to handle 10 students or 1,000 with identical overhead. Growth stops being an operational problem and becomes a marketing problem.
How Much Tribal Knowledge
Is Trapped in Your Spreadsheets?
If your competitive advantage lives in a spreadsheet only three people understand, it's not an asset—it's a liability. Let's turn it into AI.
Schedule Your Void Audit