SAT and ACT academies first
Turn diagnostics, mock-test data, tutor judgment, homework, and parent updates into one daily action plan.
Summit SAT Academy demo
Configurable AI for education businesses
Turn homework, score reports, advising tasks, and parent updates into one branded student action plan.
LearnerLoop helps SAT academies, tutoring businesses, and college consultants keep their program working between sessions, with one next action for students, parent-safe progress, and staff escalation when a human should step in.
Student Model preview
LiveCurrent goal
Raise SAT Reading/Writing to 760
Growth area
Inference plateau at 62%
Next action
24 min internal practice set
4-week pilot
1 workflow, 10-25 students
Buyer metrics
Completion, parent trust, staff time
Human oversight
Escalate only what needs staff
Focused first on SAT/test-prep academies and boutique college consulting firms.SAT academiescollege consultantsstudent follow-throughparent visibilitystaff leverage
One platform, many education businesses
Your program should keep working after the session ends.
Students forget what to do next. Parents ask for updates. Staff chase missed work. LearnerLoop turns your curriculum, advising process, parent rules, and staff judgment into daily student action.
College consulting firms first
Turn goals, activities, deadlines, opportunity discovery, and parent updates into a guided roadmap.
Northstar College Consulting demo
Tutoring, writing, and enrichment next
LearnerLoop expands when a program has repeatable student follow-through, parent visibility, and staff escalation needs.
Same configurable operating model
The demo that sells the idea
Change the business configuration. Watch the student product change.
In a live walkthrough, LearnerLoop switches from a SAT academy to a college consulting firm without custom tenant code. Buyers see their own branded product taking shape.
Student Model tracks goals, mastery, workload, context, and signals
Business-language AI behavior controls
Tenant-owned resource recommendations
Student Today experience with one next action
Balanced parent progress visibility
Staff intervention queue for human attention
Product proof
The sales moment is configuration becoming student action.
LearnerLoop should not be evaluated as a generic chatbot. The proof is whether an education business can encode its program, resources, parent rules, and staff judgment, then see those controls shape the student and parent experience.
Studio01
Configure the business system
Set brand, active modules, teaching style, workload cap, parent visibility, resources, and intervention rules in business language.
Student02
Recommend one next action
Student Today explains the highest-impact task, why it was selected, how long it should take, and which resource it uses.
Parent03
Show progress without surveillance
Parents see completion, trends, upcoming milestones, and whether action is needed without raw chats or minute-by-minute monitoring.
Staff04
Escalate only what needs a human
The intervention queue separates routine AI handling from tutor, advisor, or parent attention.
What is real today
A guided pilot proof environment, not a slide deck.
Two seeded demo tenants: Summit SAT Academy and Northstar College Consulting
Live configuration changes affect modules, branding, parent visibility, workload, and student navigation
Actual app screenshots and a 67-second recorded walkthrough
No claimed customer deployments yet; this is a design-partner proof environment
Product proof frames
The current MVP already shows the buyer workflow.
These are demo surfaces, not customer deployments. They show the product motion a qualified prospect should evaluate in a live walkthrough.
LearnerLoop Studio
Program, resource, parent, and staff rules become configuration.
Student Today
The student sees one next action with the reason behind it.
Parent Progress
Parents see progress and suggested action without surveillance.
Staff Queue
Staff sees what needs human attention and what LearnerLoop can handle.
What a buyer should see in demo
Summit SAT
SAT Practice, Mock Tests, AI Tutor, Daily Plan, Parent Progress, Accountability
Northstar Consulting
Goals, Activities, Opportunity Discovery, Admissions Roadmap, Parent Progress, AI Advisor
Business outcomes to validate
Increase student follow-through between sessions
Reduce manual parent-update and reminder work
Make the institution's method feel like a branded product
Surface which students need staff attention before problems compound
Walkthrough video
A short proof clip prospects can watch before booking.
The recorded walkthrough shows the public pitch, the live tenant switch, program configuration, and the resulting student experience. It gives cold prospects enough context to decide whether a live pilot conversation is worth their time.
The Student Model
The memory and decision layer behind every recommendation.
LearnerLoop does not just ask an AI what to say next. It maintains a structured profile of each student: goals, mastery, workload, deadlines, missed work, parent visibility rules, and staff escalation status. That model decides what the student should do today, what parents should know, and when a human should step in.
If a SAT student misses two reading tasks, has an inference plateau, and has a mock test in 10 days, LearnerLoop can recommend a 24-minute inference drill from your resource bank, explain why, update mastery after completion, and hold parent notification unless the escalation threshold is crossed.
For buyers: less manual follow-up and clearer parent confidence.
For students: fewer choices, clearer reasoning, and a task that fits today's context.
For staff: a structured record of why LearnerLoop recommended, held, or escalated an action.
Goals and milestones
Target SAT score, admissions timeline, activity roadmap, mock-test date, or program-specific outcome.
Mastery and growth areas
Skill strengths, weak spots, plateau patterns, recent mistakes, and the resources most likely to help.
Current context
Workload cap, missed tasks, upcoming deadlines, weekly completion, and what the student can realistically do today.
Visibility and escalation
What parents should see, what LearnerLoop can handle automatically, and what needs a tutor or advisor.
Product system
Everything points students to the next best action.
1
Model
LearnerLoop keeps a structured picture of goals, mastery, workload, milestones, and recent student signals.
2
Decide
Business rules and student context choose one next action, not a generic AI suggestion list.
3
Execute
The student sees a focused workflow from your curriculum, roadmap, or advising playbook.
4
Update
Completion, misses, mastery shifts, parent visibility, and intervention status feed the next decision.
Trust and AI boundaries
Designed for student and parent data from the start.
LearnerLoop is currently an MVP for guided pilots, not a claim of full enterprise compliance. The product should still make the operating boundaries clear before any institution shares real student data.
Institution-owned context
LearnerLoop is configured around the business's curriculum, resources, visibility rules, and escalation policies.
Human oversight by design
Routine work can be handled automatically, while risk patterns and important decisions are routed to staff.
Parent-safe defaults
Parents get progress, trends, missed-work context, and action guidance without private student reflections or raw AI chats.
Pilot data discipline
The first pilots should use narrow workflows, explicit permissions, tenant separation, and clear deletion/export expectations.
Buyer FAQ
Does LearnerLoop replace tutors, advisors, or staff?
No. The pilot should prove where LearnerLoop can automate routine next-action guidance and where staff should review, intervene, or decide.
What parent data should be visible?
Balanced visibility is the default: progress, trends, missed-work context, and suggested parent action. Private student reflections and raw AI chats should not be exposed by default.
What student data is needed for a pilot?
Start narrow: one program, sample resources, sample student states or anonymized cases, parent visibility rules, and success metrics. Real student data should only be used with explicit pilot permissions.
What is AI allowed to do?
AI can explain plans, summarize progress, tutor, and suggest adaptations. Deterministic product logic should control permissions, module availability, completion, scoring, and escalation thresholds.
Does LearnerLoop train models on pilot student data?
For pilots, LearnerLoop should be operated with explicit AI-use boundaries before real student data is used, including whether data may be sent to model providers and whether it may be retained or used for training.
How should minors and parent consent be handled?
A pilot should define who the institution is serving, what consent path is required, what parents can see, and which staff members can access student records before any real student account is created.
Can pilot data be deleted or exported?
Pilot terms should specify retention, deletion, and export before launch. The MVP should keep tenant-owned records attributable to an institution so pilot data boundaries can be reviewed.
Is LearnerLoop claiming FERPA, COPPA, or enterprise compliance today?
No. The current site is for guided design-partner pilots. Compliance posture, contracts, subprocessors, and production controls need to be finalized before broad rollout with real student data.
Pilot offer
Run a narrow design-partner pilot in 4 weeks.
We configure your branded student experience around your curriculum, parent reporting rules, and staff escalation process. The pilot goal is not broad deployment. It is to test whether one real workflow improves student follow-through, parent confidence, and staff leverage.
Scope
1 workflow, not a broad rollout
Participants
10-25 students or anonymized cases
Setup
3-10 resources plus parent/staff rules
Decision
Continue only if metrics justify it
Branded student and parent experience
One configured program: SAT/test prep or admissions advising first
10-25 students or anonymized sample cases
Your resource library and recommendation rules
Parent visibility and staff intervention workflow
Weekly review of usage, completion, staff time, and workflow fit
What we need from a pilot partner
One program syllabus, roadmap, or curriculum flow
3-10 representative resources or assignments
10-25 pilot students or anonymized sample cases
Parent visibility policy and escalation threshold
One owner who can review the configured pilot weekly
Written agreement on data use, deletion, and go/no-go metrics
Week 1
Map one real program, core resources, parent rules, and success metrics.
Week 2
Configure branded Studio, Student Today, parent visibility, and staff queue.
Week 3
Run guided feedback with sample students, parents, or staff.
Week 4
Review completion, parent confidence, staff time, and next pilot scope.
Pilot success metrics
Student weekly completion
Parent update confidence
Staff follow-up time
Next-action acceptance
Workflow fit for one real program