Scenely · EdTech AI module
Lesson scripts into narrated, on-brand microlearning videos.
Scenely turns a lesson script into a short narrated video in your customer's brand template, with captions, ready for review.
A reference build, not software you license. It is built to ship under your product's name.
admin.northstarcampus.com / lessons / disclosure-rule / video
Northstar Workforce Academy · Scenely
DemoStep 1 of 3
Illustrative demo on a fictional institution. Not client work.
The problem
Learners want short video; producing it by hand for hundreds of lessons does not scale, and every customer wants their own branding.
The worked example
Northstar Workforce Academy (fictional). The academy pastes a six-paragraph lesson on a new disclosure rule for its agents.
Takes lesson text from your authoring tool or API, splits it into scenes, adds narration from a licensed stock voice or one the customer approves, burns in captions and exports a video file plus a caption file to your media library. Each customer's template is configured once. Nothing publishes until an instructor approves. No avatars, likenesses or cloned voices.
From lesson text to video
Paste a lesson. Get a captioned, narrated video in your customer's template.
Press the button to turn the lesson on the left into the video on the right. As it plays, the paragraph each scene came from lights up.
Lesson text · Northstar Workforce Academy
InputThe new disclosure rule: what changes
- From 1 January, agents must give every client a written disclosure of fees and commission before the client signs anything. This lesson covers who the rule applies to, what goes in the disclosure and how to keep a record.
- The rule applies to every licensed agent. It covers new policies and renewals alike, including renewals that were agreed verbally last year.
- The disclosure lists your commission, any fees the client pays, and who pays each one. Write it in plain language. Avoid percentages without the amount they produce.
- The rule text reads: "Disclosure must be provided to the client in writing before the client signs." A disclosure given after signing does not count, even on the same day.
- Keep the signed disclosure in the client file for five years. Auditors can ask for it at any time, and a missing record is treated as a missed disclosure.
- To recap: disclose in writing, before signing, on every policy and renewal, and keep the record. Next, a two-question check.
6 paragraphs in. One captioned, narrated video out.
Illustrative demo on a fictional institution. Not client work.
AI inside the workflow
Scenely inside the product, not a chatbot beside it.
Switch between the product as it ships today and the same screen with the module working inside it. Ask a question, then approve what it prepares.
Northstar Learn · fictional product
Northstar Learn today: new lessons stay text-only until someone has time to produce video.
The assistant runs Scenely, branded as Northstar Studio, the way it is built to ship under your product's name.
Illustrative demo on a fictional institution. Not client work.
What it does
What Scenely does inside your product.
Best fit: Microlearning, corporate LMS, courseware and CE.
Script to storyboard
Splits a lesson into scenes with on-screen text and visuals.
Narration
Natural voice-over in a chosen voice, with captions.
Brand templates
Each customer's colors, fonts and intro/outro applied automatically.
Review before publish
Edit any scene or line and re-render.
How it works
One AI feature, working inside your product, in two weeks. $10,000, fixed.
In two weeks: Scenely working in your staging environment behind a feature flag, rendering ten of your lessons in two customer templates, delivered as pull requests, with an instructor review of each video, a cost per minute of video and a fixed production quote.
- 01
EdTech Product Review
30 minutes, free
We look at your product with you and pick the AI feature that fits your next release, what it would cost to serve, and what your customers' security review will ask.
- 02
The Sprint
$10,000 fixed · setup week, then two weeks of build
In the setup week we agree the feature, the eval cases and the data, and you grant staging and repository access; no build time is used. Then two weeks of build: one AI feature working inside your product, running in your staging environment on your catalog, published documents or synthetic data, behind a feature flag, delivered as pull requests. You also get quality and safety evals, a cost estimate on two models, a data-flow sheet and a fixed-price quote for production.
- 03
Targeted Unlock, then Capability Waves
Quoted at the end of the Sprint
The first production capability, delivered by pull request into your repo with no standing production access. Then the next module, on the foundation the first one built.
Built for education data
Controls your customers' security reviews ask about.
Each control is a design fact about how the module is built and how we work, not a compliance claim.
- Reads your catalog, courses, rosters and gradebook through the APIs and roles your product already has. No rebuild.
- Sprint work uses synthetic or de-identified data and ships as pull requests to your repo. No production student data.
- Answers cite the catalog page, policy or help article they came from; anything that changes a grade, an application or a student record waits for staff approval.
- Built around FERPA, COPPA, accessibility and institutional requirements.
- Built to ship under your product's name. Model and hosting are your choice.
We build around your FERPA, COPPA, accessibility, security and institutional requirements. Compliance depends on the complete product, contracts, policies and operating environment; Datics does not present software engineering alone as legal certification.
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See Scenely on your own product.
Bring one product, workflow or AI initiative. In 30 minutes we pick the module that fits first, what it would cost to serve and what your customers' security review will ask.
Calls run in US Central time. No generic AI presentation: we discuss your product, users and production constraints.