AI for EdTech

Cohortly · EdTech AI module

Ask your enrollment and retention data a question in plain English.

Cohortly lets program leads and admins ask questions of enrollment, completion, retention and revenue data in plain English and get a table or chart back, scoped to what each user is already allowed to see.

A reference build, not software you license. It ships under your product's name.

admin.northstarcampus.com / reports / ask

Northstar Workforce Academy · Cohortly

Demo
  • Which compliance courses are behind schedule for the March deadline, by department?

Step 1 of 3

Illustrative demo on a fictional institution. Not client work.

The problem

The data is in the product, but every new question (which cohort is slipping, which program fills last, where learners drop) becomes a report request. Customers want answers inside the product they already pay for, and a generic chatbot does not know what 'active learner' means for this institution.

The worked example

Northstar Workforce Academy (fictional). An employer admin asks which compliance courses are behind schedule for the March deadline.

Natural-language-to-SQL over your reporting database or warehouse, with a semantic layer for each institution's definitions, charting, and row-level permissions inherited from your product.

AI inside the workflow

Cohortly 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: admins export spreadsheets and build the numbers by hand.

No assistant

The product today. Every answer is reached by filtering, reading and clicking.

admin.northstarcampus.com / reports / compliance

Northstar LearnDashboardCoursesLearnersPracticeReportsAdmin
ReportsComplianceCompletionEnrollmentRevenueExports

Compliance training by department

5 of 5 records

Export
Search

Completion by week

All regions · last 8 periods

RefDepartmentStatus
DEP-01WarehouseOn track
DEP-02Field ServiceOn track
DEP-03Retail OpsOn track
DEP-04CorporateOn track
DEP-05LogisticsOn track

Export, filter in a spreadsheet, compare by hand, write the email yourself.

The assistant runs Cohortly, branded as Northstar Insights, the way it would ship under your product's name.

Illustrative demo on a fictional institution. Not client work.

What it does

What Cohortly does inside your product.

Best fit: SIS, analytics, student success.

Plain-English questions

Turns a question into a query over your data and returns a table or chart, with the query visible for anyone who wants to check it.

Institution context

Learns each institution's definitions (term, cohort, active, at-risk) so the same word means the right thing.

Summaries

Writes a short weekly read-out for each program lead.

Permissions kept

Answers only from data the user can already see in your product.

Where it comes from: Built on the text-to-SQL engine from a business-intelligence platform we built.

See the analytics platform it builds on

How it works

One AI feature, working inside your product, in two weeks. $10,000, fixed.

In two weeks: Cohortly answering twenty of your customers' real reporting questions on a de-identified copy of your schema, with an accuracy check on each.

  1. 01

    EdTech Product Review

    30 minutes, free

    We look at your product with you and pick the module or capability that protects your next release, what it would cost to serve, and what your customers' security review will ask.

  2. 02

    The Sprint

    $10,000, two weeks, fixed

    One AI feature working inside your product: running in your staging environment on your catalog, 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.

  3. 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.

  • Runs on your catalog, documents or data through the APIs your product already exposes. No rebuild.
  • Sprint work uses synthetic or de-identified data and ships as pull requests to your repo. No production student data.
  • Every answer that matters shows its source; material actions go to a person for 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.

How we handle student data

Work this builds on

Projects we have delivered that use the same engine or the same engineering pattern.

Plain-English Analytics

Business Intelligence SaaS · Analytics Chat / Natural-Language Data

Plain-English questions answered as tables and charts, within each organization's data

A BI platform where business users ask questions in plain English and get SQL-backed tables, charts and summaries from their own databases.

Conversational analyticsText to SQLDashboards
Read case study

Adaptive Intake Platform

Healthcare SaaS · Adaptive Intake / Multi-Tenant SaaS

Adaptive intake taken from a one-site prototype to a multi-tenant product

Intake that adapts its questions and writes a structured summary, taken from a single-practice prototype to a multi-tenant SaaS with self-serve onboarding, billing and full isolation.

Adaptive intakeMulti-tenant SaaSData isolation
Read case study

Controlled Record Release

LegalTech · Records / Controlled Release and Audit

Sensitive records discovered, requested and released under control, with a full audit log

Metadata-first discovery, AI extraction from archives, a tracked request lifecycle, payment and controlled release of sensitive records with audit logging.

RecordsDocument extractionAudit log
Read case study

See Cohortly 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.