Software development and AI pod teams for edtech

Ryz staffs senior Latin American engineering teams for edtech companies, universities and corporate learning providers: learning platforms, LTI and rostering integrations, assessment engines and learning data pipelines. Our AI pod teams build tutoring, feedback and content tools with student-safe guardrails, in your cloud, and ship them to production. Teams work on US business hours, which matters when teachers report problems during the school day.

Edtech has a hard calendar. August and January back-to-school rushes bring rostering syncs, SSO problems and load spikes in the same week, and district procurement asks detailed privacy questions before anyone signs. Trusted by Fortune 500 engineering teams, Ryz brings engineers from the top 1% of the tens of thousands we interview, and they work on your team, in your repos and sprint rituals.

What we build for edtech teams

• Learning platforms. Course delivery, content players, progress tracking and teacher dashboards on web and mobile, built to work on low-end Chromebooks and spotty school Wi-Fi.
• LTI 1.3 and LTI Advantage tools. Launches, Deep Linking, Names and Role Provisioning Services and Assignment and Grade Services for Canvas, Schoology, Brightspace, Blackboard and Moodle, plus 1EdTech certification preparation.
• Rostering and SSO. OneRoster 1.2 CSV and REST imports, Clever and ClassLink integrations, Google and Microsoft SSO, and nightly sync jobs that reconcile section changes without losing student work.
• Assessment engines. Item libraries in QTI 3.0, adaptive testing, accommodations such as extended time and text-to-speech, secure delivery and score reporting.
• Learning data pipelines. xAPI and Caliper events into a learning record store, Ed-Fi for state and district data exchange, and warehouse models for engagement and outcomes.
• Higher ed and enrollment systems. Integrations with Ellucian Banner, Workday Student and Slate, application portals, and student success dashboards for advisors.
• Corporate learning. SCORM and xAPI packaging, LXP integrations with Workday Learning, Cornerstone and Docebo, and certification tracking.
• Accessibility work. Screen reader testing, keyboard navigation, math rendering with MathML and accessible charts, built into components instead of patched later.

Where AI pods help in edtech

Education AI carries extra weight: wrong answers teach the wrong thing, and the users are often minors. Our AI pod teams build evaluation and guardrails before features reach students.

• Tutoring assistants. Assistants grounded in your curriculum through retrieval, built to guide rather than hand over answers. The engineering work is pedagogy-aware prompting, content filters for minors, and evaluation sets written with your learning designers.
• Writing and short-answer feedback. Rubric-aligned feedback drafts that teachers approve or edit. Pods measure agreement with human graders and check for bias across student groups before scores influence anything.
• Content and item generation. Drafting practice questions, distractors and leveled readings tagged to standards such as Common Core or NGSS, with subject-matter experts reviewing every item before it enters the item library.
• Multilingual student and family support. Agents that answer enrollment, billing and technical questions in several languages and hand off to staff. Our pods have shipped a driver-support agent that works in three languages and an AI voice platform with more than 1M outbound calls; see the case studies.
• Early-alert models. Predicting which learners are at risk of dropping a course from LMS activity, so advisors reach out sooner. Explainability and fairness testing matter more than raw accuracy here.

Regulations and constraints our engineers work within

• FERPA. Education records shared with vendors under the school official exception, with use limited to the purpose the school authorized. This drives data minimization, retention and deletion design.
• COPPA. Services directed at children under 13 need verifiable parental consent or school authorization in the classroom context, and the FTC's amended COPPA rule tightened requirements further.
• State student privacy laws. California's SOPIPA, New York Education Law 2-d, Illinois SOPPA and others, plus district data privacy agreements such as the Student Data Privacy Consortium's National DPA.
• PPRA. Limits on surveys that ask students about protected topics, relevant to wellbeing and climate survey features.
• Accessibility. Section 508, WCAG 2.1 AA, and the Department of Justice's ADA Title II rule that sets WCAG 2.1 AA for public schools and universities, which flows down to their vendors.
• GDPR and international rules for products sold outside the US.

Our engineers have experience working within these requirements under your policies and controls. Ryz does not claim certifications on behalf of its teams.

Integrations and data

• LMS: Canvas, Schoology, Brightspace, Blackboard Learn, Moodle and Google Classroom.
• SIS: PowerSchool, Infinite Campus, Skyward, Ellucian Banner and Workday Student.
• Standards: LTI 1.3, OneRoster, QTI, Caliper, xAPI, SCORM, Ed-Fi and CASE for academic standards.
• Identity: Clever, ClassLink, Google Workspace for Education and Microsoft Entra ID.
• Data: Snowflake, BigQuery or Databricks warehouses with dbt models, built by our data engineering teams.

The failure modes are familiar to anyone who has run an edtech platform through a fall term: a district changes its SIS mid-year and section IDs shift, an LMS update breaks a Deep Linking flow, or a rostering sync deletes enrollments that teachers had graded. Our engineers build idempotent sync jobs, soft deletes, per-district configuration and replayable event logs so those incidents become a quick fix instead of a data recovery project.

How teams engage Ryz

• Staff augmentation. Add senior full-stack, mobile or data engineers to your team before back-to-school. See hire edtech developers.
• Dedicated development team. A Ryz team owns a scoped system, such as an LTI Advantage integration suite, an assessment engine or a rostering rewrite.
• AI pod. A pod of senior engineers, including a tech lead and an ML engineer, builds tutoring or feedback features with evaluation and guardrails, then ships them to production.

The steps are Talk, Match, Join and Grow. Typical cost is $7,000–$15,000 per engineer per month: mid-level $7,000–$10,000, senior $10,000–$15,000, leads $15,000+. Three senior engineers at $11,000 a month for six months is $198,000. You get a plan, a price and the names of the people before you start.

When Ryz isn't the right fit

• You need instructional designers or curriculum writers rather than engineers.
• You want a self-serve freelancer for a short LMS plugin, booked by the hour.
• You need teams in Europe or Asia time zones for regional school calendars, or a packaged AI tutoring platform rather than a team that builds.

Related

• AI chatbot development
• SaaS development
• LLM development
• Media and entertainment software teams
• RAG vs fine-tuning

FAQ

Do your engineers know LTI 1.3 and OneRoster?

Yes. Our engineers build LTI 1.3 and LTI Advantage tools, including Deep Linking and grade passback, and OneRoster and Clever rostering. They test against the major LMSs and help prepare for 1EdTech certification.

How do your engineers handle student data?

They work in your environment, under your access controls, data privacy agreements and retention rules, and student data stays in your accounts. They design for FERPA and COPPA from the start: minimal collection, purpose limits and deletion on request. Ryz does not claim certifications; our engineers have experience working within these laws and your district agreements.

Can AI tutoring be safe for K-12 students?

It can be made much safer with grounding in approved content, content filters, refusal behavior for off-topic requests, logging the school can review, and evaluation sets built with teachers. Our pods treat those as launch requirements.

What does an edtech engineering team cost?

Typical rates are $7,000–$15,000 per engineer per month by seniority. Cost is team size times months times rate; two senior and two mid-level engineers at $12,000 and $8,500 for four months is $164,000.

Can you help us get ready for the back-to-school peak?

Yes. Our teams work on US business hours and plan rostering dry runs, load tests and on-call coverage with you for August and January, so fixes land the same day teachers report issues.

Questions we didn't answer? Email info@ryzlabs.com.