For teachers
Your methodology deserves infrastructure.
StoryLab builds a three-layer AI agent from your teaching DNA—one that reads the room, decides what to do, and speaks in your verified voice. Not a chatbot with your bio pasted in. A cognitive model of how you teach.
The reality
The best teachers in the world are underpaid. And then they leave.
44%
of teachers leave within 5 years
$65K
average teacher salary — while rent doubles
30
students max — your impact is capped by hours
When a great teacher quits — burnout, career change, retirement — their pedagogy disappears. The specific way they see students, the moves they’ve developed over years, the instincts that made them irreplaceable. Gone. Their students lose access to something no AI chatbot can replicate.
The opportunity
Your pedagogy should outlive your schedule.
StoryLab extracts your teaching methodology into a three-layer AI agent. Every turn with a student, your agent runs the same cognitive loop you do: observe the student, decide what to do, speak in your voice. Your pedagogy stays alive, keeps affecting students, and generates passive revenue.
Whether you’re teaching full-time, scaling a private practice, or retired and wanting your methods to keep working — your agent carries your instincts forward. And a discriminator loop verifies it actually sounds like you before it goes live.
The architecture
Three layers. Every turn. The same loop you run in your head.
Most AI teaching tools are a single prompt: “You are a helpful tutor.” Your agent is a three-layer cognitive model that mirrors how real teachers actually think before they speak.
Layer 1
Observe
Read the room. Every turn, your agent detects: Did the last thing I said land? Is this student deflecting? Are they close to a breakthrough or checked out? What concept are they circling? What emotion are they in? This runs before any response is generated.
Layer 2
Orient
Decide what to do. Based on what it observed, your agent chooses: push or listen? Lead with a story or a question? Shift voice registers—from warm to direct, from exploratory to focused? Which of your signature moves to deploy, and in what order?
Layer 3
Respond
Speak in your voice. Not an AI approximation—your actual voice, trained on 15–20 exemplar exchanges extracted from your real teaching. A discriminator loop scores voice fidelity on a 10-point scale. If it doesn’t sound like you, it doesn’t go live.
Why three layers matter:A single-prompt chatbot can’t read the room—it just generates the next response. Separating observation from decision from voice means your agent can notice a student is deflecting, decide to back off and tell a story instead, and deliver it in your exact register. That’s teaching. That’s what we’re building.
The agent builder
Built from your materials and a conversation.
Upload syllabi, session transcripts, or marked-up student work. Then have a 10-minute conversation with our AI interviewer. The system extracts everything it needs to populate all three layers of your agent.
Observe config — What you notice before you intervene: resonance signals, diagnostic hierarchy, concept vocabulary
Orient config — How you sequence teaching: pacing style, opening moves, push-vs-listen rules, voice registers, story deployment
Voice exemplars — 15–20 real student/teacher exchanges extracted from your corpus, scored by a discriminator for voice fidelity
Student types & moves — The types of students you see, your signature coaching moves, and which moves work for which students
For everyone who teaches
You don’t have a teaching degree. You have a methodology you haven’t named yet.
You tutor friends’ kids. You coach at a nonprofit. You have 15 years of expertise and no platform for it. You’re not a “teacher” in the credentialed sense — but you have a way of seeing students that nobody else has.
StoryLab’s agent builder doesn’t ask for syllabi or transcripts. It asks how you teach. If you can answer that question with specificity, you can build an agent. The conversation is the credential.
Build your agent →