AI Faculty Coach

For

Heads of teaching and learning at independent schools

Solves

Teachers want to teach creativity and resilience but lack the training and time to design these lessons.

Delivers

Trained staff, feedback reports, lesson plans

Built in

10 days of creation time, MVP in 2 days

Investment

$5,000 for the MVP, $16,000 for the full product

Run it

Inside your business, or as part of your offer to clients

For heads of teaching and learning at independent schools, turn teacher profiles and lesson plans into trained staff capable of delivering life skills.

1. Simulate classroom scenarios.
2. Adapt training to subject and grade level.
3. Provide line-by-line feedback on reflections.
4. Deliver micro-lessons via text and voice.
5. Track cohort progress and blockers.
6. Generate progress reports for school leadership.

What goes in, what comes out

The workflow

1. In
   Start with

   Teacher profiles, lesson plans, classroom recordings or written reflections

2. 1

   Select a module

3. 2

   Complete a micro-lesson

4. 3

   Practice a simulation

5. 4

   Submit a classroom reflection

6. 5

   Receive AI feedback

7. 6

   Review cohort notes

8. 7

   Apply techniques in class

9. Out
   Finish with

   Trained staff, feedback reports, lesson plans

AI does the heavy lifting, people stay in charge

Use language models to interpret lesson plans and generate role-play scenarios. Keep pedagogical frameworks in structured fields. Validate feedback tone and accuracy through deterministic checks. A pedagogy expert confirms the AI's coaching quality and relevance before deployment.

What your team sees

Key screens: Module dashboard, simulation interface, feedback review. Use a central simulation canvas for the role-play and a right-hand panel for references, constraints and feedback. Display the scenario text and the AI's response. Show the teacher's input and the AI's evaluation. In this product, the first view is the module dashboard, followed by the simulation interface and feedback review.

Accounts and administration

User accounts, module versions, coaching session history, approval states for feedback, audit trail

Integrations and data access

Learning Management Systems, HR databases

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   2 days

   One buyer segment, one recurring use case. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   3 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   5 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will heads of teaching and learning at independent schools use it to solve "teachers want to teach creativity and resilience but lack the training and time to design these lessons"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. The pilot proves value by comparing teacher retention and training costs against current methods.
4. Measure, then decide. Track teacher retention rates and student engagement scores. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. One buyer (independent school), one use case (financial literacy), first two modules, manual review of feedback

After the MVP. Automated cohort meetings, integration with LMS, advanced analytics on student outcomes

What the build depends on. LLM APIs, voice synthesis engines, pedagogical data sets

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test pricing at 500 dollars per teacher per year

Message to test

Train your staff in life skills without the scheduling headaches

Where to find buyers

School boards, EdTech conferences, LinkedIn

Lead magnet

A free demonstration of a financial literacy module

The first 30 days

1. Week 1: Ship one 4-week module on teaching financial literacy.
2. Week 2: Fine-tune the AI on a specific pedagogy.
3. Week 3: Launch the platform for one teacher.
4. Week 4: Collect audio clips and provide manual feedback.

Paid pilot

The pilot proves value by comparing teacher retention and training costs against current methods. The baseline is the cost of in-person workshops. The decision is whether the license model is viable.

Success metrics

Teacher retention rates and student engagement scores

Retention and expansion

Earn through annual licenses and new module releases

Why clients would pick it

Fine-tuning on specific pedagogies creates a moat. Data on successful coaching interactions improves the model over time.

Alternatives and positioning

People use recorded videos and static forums. This differs by offering interactive role-play and real-time feedback.

Main delivery costs

Compute costs for LLM inference and voice synthesis

The AI must not grade students or replace human judgment. It must respect data privacy