Research mentorship · Interview prep

Land a research mentor.

Describe your project. LabBound finds the right professors, drafts the cold emails that get replies, and rehearses the interview with an AI coach that scores every answer.

Free to start, no credit card · every professor we surface is a real, published researcher — pulled from public research databases, never invented.

Built by ISEF finalists

We cold-emailed our way into research labs the hard way, then built the tool we wish we'd had. The friends we built it with have already used LabBound to land their own mentors.

1,000+students
50+schools
10k+professors found
I got a mentor at Yale with the help of this app.
Student researcher · now mentored at Yale
The interview practice helped me qualify for ISEF. 90% of your score comes from you being able to present, and nothing else is better than this.
Student researcher · ISEF qualifier

Built for research in every field

NeuroscienceCRISPR & gene editingMachine learningClimate & ecologyCancer biologyMaterials scienceAstrophysicsPublic health NeuroscienceCRISPR & gene editingMachine learningClimate & ecologyCancer biologyMaterials scienceAstrophysicsPublic health

The real problem

A mentor is a numbers game most students lose.

The students who land research aren't smarter — they send better emails, to the right people, and they don't freeze when the professor asks a hard question. LabBound is the unfair advantage for the rest: it does the finding, drafts the ask, and drills you until you sound like you belong in the room.

Inside LabBound

This is the actual product.

Not a mockup — real screens from a real project: eight California CRISPR labs found, a drafted email, and a scored interview.

labbound.app/app
LabBound cold email tab: California professors ranked by research fit, with locations and priority scores LabBound: a personalized cold email drafted for a specific professor LabBound practice log: per-question scores for clarity, specificity, scientific thinking, delivery and composure LabBound interview coach setup: mode, difficulty, coach style and project brief

Professor names come from public research databases — the same way you'd find them yourself, just ranked by how well their papers match your project.

01 · Find

The right professors, ranked by real fit.

Describe your project and LabBound surfaces the professors whose published research actually overlaps yours — not a generic list off Google you'd waste emails on.

  • Ranked by how closely their papers match your work
  • Filter by country, state, or city for in-person labs
  • Every result links to their real profile and recent papers
3 strong matches for “CRISPR off-target prediction”
Dr. Maya Chen
UC Berkeley · Computational Genomics
94%
Dr. Aaron Delgado
Stanford · Bioengineering
88%
Dr. Priya Nair
MIT · ML + Biology
81%

02 · Reach out

Cold emails that actually get replies.

LabBound drafts a short, specific email for each professor — in a real student's voice, referencing their actual work. You edit it and send from your own inbox. The specificity is exactly what earns a reply.

  • References the professor's real papers, not filler
  • One clear, small ask — the kind that gets a “yes”
  • Yours to edit and send; never blasted automatically
To: Dr. Elena Chen · UC Berkeley  ·  From: you@school.edu  ·  Draft — yours to edit

Lightweight graph model for CRISPR off-target prediction

Dear Dr. Chen, I'm an 11th-grader at Westbrook High School working on a lightweight graph neural network to predict CRISPR off-target sites. Right now I'm comparing it against CFD and Elevation on held-out guides, and testing whether chromatin accessibility helps at low read depth. The main issue is training-set bias toward highly expressed genes. I read your 2024 work evaluating off-target scoring methods. Since you've directly compared algorithms like CFD and Elevation, I'd love to understand what you've learned about validation strategy — especially how to handle class imbalance and performance variation across chromatin contexts. Would you be open to a brief conversation about what makes off-target prediction models useful in practice, or could you point me toward someone in your lab working on this? Thank you for considering this. Sincerely, Maya Ellison

03 · Walk in ready

Rehearse the interview. Get scored.

When a professor says yes — or a judge walks up to your poster — you can't freeze. The AI voice coach asks real questions, you answer out loud, and every answer gets scored like a tough, fair mentor would.

  • Voice-first: answer out loud, flip your slides
  • Scored on clarity, specificity, thinking, delivery, composure
  • One concrete thing to fix after every answer
Score card · Q2

“How would you convince a skeptic your control is fair?”

Clarity
4
Specificity
3
Sci. thinking
4
Delivery
3
Composure
4

Fix next: name the control condition in one sentence, then the confound you ruled out.

The difference

Couldn't I just use ChatGPT?

You could — and you'd still be doing the hard parts yourself. LabBound does them for you.

Just a chatbot

You + ChatGPT

  • You still have to find the right professors yourself
  • Generic email that reads like a template
  • No idea who actually fits your specific project
  • You "practice" by typing to yourself
  • No feedback on how you actually come across

Built for this

You + LabBound

  • Professors ranked by how their real papers match yours
  • Each email cites their actual, recent work
  • Filtered to your field — and your city, if you want one nearby
  • A voice coach that scores every answer out loud
  • One concrete thing to fix after every question

How it works

From “no idea where to start” to walking in ready.

1

Describe your project

Paste a rough brief or your abstract, upload a poster or deck. No finished project required.

2

Find & reach out

Get ranked professors and a personalized cold email for each. Edit, then send from your inbox.

3

Rehearse & score

Run a full voice mock interview and read the score card — then fix the one thing that matters.

Who it's for

Built for the student. Clear for the parent.

This isn't an open-ended chatbot to get lost in. It's structured practice with a beginning, a middle, and a score.

For students

Stop freezing up

Rehearse until "why this method?" doesn't rattle you, and reach mentors who'd actually want to hear from you — without burning your best emails on the wrong people.

For parents

One price, no surprises

A single simple price with hard usage caps, so costs can't run away. You can see exactly what they're practicing and working toward — real skills, not a homework shortcut.

For clubs & labs

Equip the whole team

Ten seats for a research club, class, or tutoring group — the same coach and mentor-finder, shared. One person buys, ten students practice.

Pricing

Start free. Go Pro when it counts.

Free

$0 no card

Feel the score card before you pay a cent.

  • 1 mock interview + score card
  • 3 professor searches
  • Text coach
Start free

Lab / school

Free right now

10 seats — the adult with the budget buys it.

  • Everything in Pro × 10
  • Clubs, counselors, programs
  • One email, ten students
Equip a lab

FAQ

Questions people ask

How does it know which professors to email?

You describe your project, and LabBound ranks professors by how closely their published research matches yours — so you reach people who'd actually care, not a random list off Google.

Won't a cold email “written by AI” get ignored as spam?

The opposite. It writes short, specific emails that reference the professor's real research and what you want to learn — specificity is what earns a reply. You send it from your own inbox after editing, so it's genuinely yours.

What does the interview coach actually score?

Every answer is rated on clarity, specificity, scientific thinking, delivery, and composure — plus the single thing to fix next. It's the honest feedback a good mentor gives, on demand, out loud.

Do I need a finished project to start?

No. Start with a rough idea or load a sample project and run a full mock interview in one click. You sharpen the real thing as you go — free, no card.

What does Pro actually include?

The AI voice coach, unlimited projects and slide uploads, 20 full mock interviews and 50 professor searches a month, and every cold-email draft. The free tier gives you one full interview and three searches so you can feel it first.

How long does a mock interview take?

About 5–10 minutes for a full round of five questions — you answer out loud and get a score card at the end. Prefer something quicker? Single-question drills take about a minute each.

Is my data safe? (A note for parents.)

We store only what the tool needs to work — your project text, drafted emails, and interview scores. We don't sell data and there are no ads. Want your account and data deleted? Email getlabbound@gmail.com and we'll remove it.

Who is it for?

High-school and early-college students chasing research mentorships, science fairs, and lab interviews — but anyone who needs to email a professor and not freeze in the conversation can use it.

Is this affiliated with my school or science fair?

No. LabBound is an independent tool built by former ISEF finalists — it isn't endorsed by or affiliated with ISEF, Regeneron, or any university. It just helps you reach and prepare for them.

See it work in ten seconds.

Type your research topic up top and watch it surface real professors — then sign up free to email them and rehearse the interview.

Free to start · no credit card · every professor is a real, public researcher.