EdTech software development

EdTech software development, from engineers who have shipped it to schools

We build learning platforms and AI features for education companies — with the school integrations, role models and student-data care that EdTech products live or die on. Our founder is a senior engineer on an AI-powered K-12 platform used daily across 20+ US schools.

What makes EdTech harder than ordinary SaaS

Selling to schools changes the engineering. Accounts are not created by users; they arrive from a district's rostering system and change every term. Single sign-on is expected. A teacher, a student, a parent, a school admin and a district admin each need a different view of the same data. And student data carries obligations a typical SaaS product never meets.

AI raises the stakes further. Generated lesson content and automated grading are only useful if teachers can trust them — which means measuring quality, not assuming it.

What we have learned building for schools

Drawn from our founder's production experience on a K-12 AI platform used across 20+ US schools.

Rostering and SSO are the front door

Integrations with rostering and identity providers such as ClassLink and Clever decide whether a district can adopt your product at all. They have to handle term changes, transfers and role changes without manual fixes.

Roles drive everything

Teacher, student, parent, school and district roles need separate apps or views over shared data, with permissions enforced on the server rather than hidden in the UI.

AI content needs a quality gate

LLM-generated lessons, questions and feedback are checked by evaluation panels (LLM-as-judge, calibrated against human review) before they reach a classroom.

Grading with AI, carefully

Automated grading pipelines work best as structured outputs with rubrics, confidence and a teacher in the loop for anything consequential.

Agents for guidance, grounded in local data

Advising and tutoring agents (built on LangGraph) are grounded with retrieval over curriculum and school-specific content, isolated per school.

Reliability during the school day

Usage spikes at the start of class. Testing, CI/CD and observability (error tracking and tracing) matter more than in most products.

What we build for education companies

Learning platforms and LMS features

Courses, assignments, progress tracking and teacher dashboards, built around how classrooms actually run.

School integrations

Rostering, SSO and gradebook integrations, so districts can adopt your product without manual account management.

AI content generation

Lesson, question and feedback generation with quality evaluation built in, so teachers review drafts rather than start from blank pages.

AI tutors and advisers

Student-facing assistants grounded in your curriculum and the school's own content, with guardrails appropriate for the age group.

Assessment tools

Assessments, rubrics and reporting — including AI-written summaries that stay consistent and avoid over-confident conclusions.

Typical projects

Getting district-ready

Adding rostering, SSO and role-based access to a product that so far only signs up individual teachers.

Adding AI to an existing platform

Content generation, grading assistance or a tutor inside a learning product you already run.

Building a new learning product

An MVP for a new education product, designed so school integrations can be added without a rewrite.

Measuring AI quality

Building evaluation sets and quality gates for AI features teachers have started to distrust.

Related work

Our founder's EdTech experience is employment work and is not presented as a client case study. The DablewDevs projects below show the same engineering in other domains: AI-written reports with a fixed structure, and assessment tooling.

How we work with EdTech teams

  1. 1

    Understand the buyer and the user

    Districts buy, teachers adopt, students use. We map what each needs before designing features.

  2. 2

    Get the data model right

    Schools, classes, terms, roles and enrolments modelled so rostering changes do not break your product.

  3. 3

    Build and evaluate

    Features delivered in short cycles; AI features shipped with an evaluation set from day one.

  4. 4

    Pilot with real classrooms

    A limited rollout with feedback from teachers before district-wide use.

Student data and deployment considerations

Student-data privacy

Products used in US schools must account for student-privacy obligations such as FERPA and COPPA, and many districts require signed data-privacy agreements. We design for data minimisation and clear data flows; your legal team confirms the compliance position.

AI and student data

Only the data a model needs is sent, with personally identifiable information removed where possible, through providers and endpoints that meet your contractual obligations.

Tenant isolation

Each school or district's data — including AI retrieval indexes — is isolated, so one customer can never see another's.

Accessibility

Interfaces built to accessibility standards, since schools must serve every student.

Technology we use

Application

  • React
  • Next.js
  • TypeScript
  • Node.js
  • Python / FastAPI
  • PostgreSQL

AI

  • Vercel AI SDK
  • LangChain
  • LangGraph
  • Anthropic Claude
  • Google Gemini
  • pgvector

Quality & operations

  • promptfoo
  • Langfuse
  • Vitest
  • Playwright
  • GitHub Actions
  • Sentry
  • OpenTelemetry

Who this is for

  • EdTech startups preparing to sell to schools and districts.
  • Education companies adding AI content generation, grading or tutoring.
  • Learning platforms whose AI features teachers do not yet trust.
  • Organisations building assessment and reporting tools.

Related services

EdTech Software Development: common questions

Tell us about your education product

Tell us what you are building or what needs fixing. You will hear back from an engineer — not a sales rep — within one business day, usually with questions or a rough approach.

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