AI for EdTech

AI for K-12 software vendors

AI for your K-12 product, built without touching student data.

Datics works for the K-12 software vendor, not for districts: we build staff-, teacher- and parent-facing AI modules inside your product on synthetic data, and your production student data stays in your US cloud, under your controls.

Built for: K-12 SaaS vendors serving districts, schools, teachers and families.

How we handle student data

These are design facts about how we work, not compliance claims. Your counsel decides how your agreements apply.

  • We work for you, the vendor, and accept the student-data obligations your district agreements flow down to anyone working on your product.
  • We build and test on synthetic or de-identified data. Production student data stays in your US cloud, under your access controls, and we hold no production credentials.
  • Children's data is never used to train models, and features follow COPPA rules for users under 13.
  • Built around FERPA and COPPA, with an approval gate before anything is written and an audit log with deletion.

Where these products get stuck

  1. 01

    Districts write the rules

    Most districts now have AI guidelines, and student privacy is the first question in every review.

  2. 02

    Teachers and parents need answers

    Help requests spike at the start of every term and the same questions arrive from every school.

  3. 03

    Admins want answers, not report requests

    District admins ask for numbers your reports don't show, and each one becomes a ticket.

How it works

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

K-12 vendors usually start with Docently for teachers and parents on their real help center, on synthetic data, because it never needs student records.

  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

Product improvements you can measure

What we agree to measure in the Sprint, so the production decision rests on your numbers, not ours.

01

Teacher and parent help

Measure

Questions answered without a ticket at term start

02

Admin answers

Measure

Report requests closed without engineering time

Proof

What we have built that this rests on.

33 case studies across 17 industries, 19 of them AI products or brownfield AI builds shipped inside software customers already use. Each EdTech module starts from one of those engines, so you are not paying for us to learn.

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

Review-and-Approve Documentation

Dental Clinical Software · Human-in-the-Loop AI / Approval Gate

AI drafts, the professional approves: nothing is released until a person signs off

Appointment audio transcribed live and drafted into structured clinical notes with codes, which the dentist reviews, edits and approves before anything is released.

Human in the loopApproval gateVoice AI
Read case study

AI Knowledge and Support Agent

Cloud Storage Software · Support Agent / Knowledge Retrieval

An AI agent that answers from knowledge spread across cloud storage, inside Slack and Teams

AI that ingests and indexes documents and pages across several cloud-storage providers and answers in Slack, checking approved answers first and falling back cleanly when it has nothing relevant.

Support agentRetrievalSlack
Read case study

Read next: K-12 software vendors can ship AI without touching student data

See one module 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.