Why do you need this?
Most CE teams are stuck with outdated systems that can’t keep up.
- Systems don’t talk to each other.
- Data is scattered and hard to use.
- Manual tasks slow everything down.
- Platforms are too rigid to support new programs.
We help you fix the foundation, so you can scale faster, work smarter, and deliver better learner outcomes.
Who we work with
What We Offer
We design, rebuild, or extend learning platforms to support new business models and modern learner experiences.
- Cloud-native infrastructure with role-based access.
- Custom LMS, LXP, and microlearning platform builds.
- SOC 2, FERPA, and HIPAA alignment for secure deployment.
- API and integration layers with SIS, HRIS, CRM, and CEU registries.
Connect your learning ecosystem and turn data into decisions with a unified architecture.
- Data integrations built on Microsoft Fabric, Databricks, or Snowflake.
- ETL pipelines across LMS, assessments, CEU systems, and credentialing platforms.
- Dashboards for learner behavior, skill progression, and compliance.
- Ready for Power BI, Tableau, or custom analytics environments.
We develop AI-powered agents and course widgets that enhance learning without compromising data security.
- Generative AI tutor and learning path agents.
- Content recommendation engines and adaptive UX widgets.
- Policy-aware design with full data governance and audit trails.
Accelerate content workflows and fuel AI/LLM pipelines with structured, enriched content.
- AI-assisted tagging and manual metadata enrichment.
- Ontology mapping to industry taxonomies (e.g., O*NET, NICE).
- Video/audio/structured content annotation for RAG and LLM use cases.
Why Magic EdTech
We understand education technology inside and out and deliver solutions built to evolve with your programs.
Cloud-native learning systems are engineered for flexibility, secure deployment, and new program models.
ETL pipelines and dashboards connect LMS, CEU, and credentialing data for real-time decisions.
Custom agents and widgets enhance learning while preserving governance, security, and compliance.
Metadata enrichment and annotation pipelines accelerate content workflows and power AI use cases.
FAQs
Stand up a standards‑based integration layer (e.g., LTI/xAPI/OneRoster) and mirror current feeds into a canonical, governed model while your existing flows keep running. We run parallel pipelines to reach field‑level parity, validate with contract tests and data‑quality rules, then cut over using feature flags and canary releases. During transition, bi‑directional sync prevents double entry; encryption, RBAC, and audit trails maintain compliance. If any KPI drifts, rollback is immediate.
We design for open standards and clear separation of concerns. Connectors sit behind an API layer, data is modeled in portable schemas, and you retain ownership of both raw and modeled datasets. Everything—from transformation logic to dashboards—is exportable, and services are containerized so you can redeploy in your preferred cloud or analytics stack later.
A strong approach is to define domain‑specific SLAs up front and wire them into your pipelines and dashboards. For example: roster and permissions nightly with 15‑minute deltas during add/drop windows; course/grade events hourly; credential status and CE credits near‑real‑time. Pipelines should use change‑data‑capture where available, log freshness and lineage on every run, and trigger alerts on threshold breaches (e.g., late feed, schema drift, null spikes). During cutover, run parallel loads to validate parity, publish a runbook with clear RTO/RPO, and surface “last updated” and success metrics in an operational dashboard so stakeholders can trust what they’re seeing.
We replace brittle extracts and spreadsheets with governed, repeatable pipelines and a single source of truth. Routine jobs—rosters, outcomes, CEU tallies, credential status—are scheduled and monitored, while self‑service dashboards answer most stakeholder questions without ad‑hoc pulls. The result is faster reporting cycles, fewer reconciliation headaches, and more time for higher‑value work.
Yes. We use multi‑tenant patterns and configuration‑as‑code so you can add divisions, geographies, or external partners via configuration rather than bespoke builds. Feature flags and load‑aware services let you handle peak terms and new offerings without compromising performance or data quality.
We enrich content with consistent metadata, map it to recognized skill/role taxonomies, and build annotation workflows that support search, recommendations, tutoring, and analytics—without exposing private data. Guardrails such as human‑in‑the‑loop review, policy checks, and traceable model inputs keep AI features useful, explainable, and within your governance policies.
Let’s Talk About Your Tech Stack
We’ll help you architect a smarter, more scalable learning ecosystem—from content operations to AI agents.