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Multilingual student support
An agent answers student and trainee questions across languages by reasoning over course content and program documentation, not a single scripted flow.
Agentic AI in education
Agents that scale student support .
We build agents that read your course content, student records, and program guidelines, then answer, route, or adapt in real time — with the escalation path an academic team will actually trust.
Trusted by operators
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+11.76% orders from day 1 of the Australian launchThree-agent product advisor guiding customers through print-order configuration.
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Supply chain intelligence agents reducing manual coordination overhead.
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37 kernels ported from CUDA to Intel GaudiProduction AI workloads moved to new hardware for on-prem LLM deployment.
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AI agent implementation for a global automotive enterprise.
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2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
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0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
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Why agentic AI
Most EdTech AI is a chatbot wearing a syllabus
Education has the shape these systems handle well: a defined body of course content or policy, a stream of individual student questions or performance signals, and a decision that improves when more than one source is checked. The common failure is treating it as a single-turn FAQ bot, which answers the easy questions and fails silently on the rest, leaving no record of what it missed or why.
every answer traces to a source document
Use cases
Where agents earn trust in education operations
Workflows with a defined knowledge base and a clear point to escalate to a person.
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Adaptive learning paths
Reads a learner's progress and prior responses, then sequences or adjusts content — flags stalled learners to an instructor instead of guessing.
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Admissions and enrollment triage
Cross-checks applications or enrollment requests against program requirements and prior records, routing edge cases to an admissions officer.
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Instructor feedback drafting
Drafts rubric-aligned feedback from a student's submission and history for an instructor to review and send — not to grade unsupervised.
The cost of unanswered questions
What a training knowledge base answers once an agent sits in front of it
ARIJ Network is the closest thing to direct education proof we have: a bilingual agent embedded in ARIJ's Moodle LMS, answering learners only from ARIJ's own knowledge base across 22 countries. It served journalist training rather than a school or university, so treat it as adjacent, not vertical-exact. The Synera and Mixam figures sit further out (engineering software and print on demand) and are here because they measure what a learner-facing agent depends on: multi-step validation instead of a single retrieval pass. Every figure below links to the case study behind it.
Agents do not grade high-stakes assessments or decide admissions on their own. They retrieve, cross-check and draft what sits before a person's decision — and escalate to an instructor or administrator, reasoning attached, when the content does not cover the question.
Knowledge-inquiry response rate before the agent
1%
Journalist training, not a school or university — ARIJ's Moodle knowledge base went largely unanswered outside a narrow set of scripted replies, a full training library learners could not actually query.
2 hrs → 3 min
to generate a validated workflow
Not an education deployment — Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass.
95.4%
Success rate in workflow results
Not an education deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations.
Client results
Proof from the closest learning-platform deployment we have
We have not yet shipped an agent inside a school, university or EdTech vendor. ARIJ Network is a learning-platform deployment in everything but the client's sector label — a bilingual agent living inside a Moodle LMS, answering learners from the institution's own knowledge base across 22 countries — built for journalist training rather than a degree program. Synera and Mixam are further out still, and are here for the mechanism, not the vertical.
Delivery path
From workflow audit to a production support agent
TriStorm keeps academic ownership and engineering aligned, so the agent launches with people who trust it.
Map workflow and content sources
We audit the target workflow, the course or policy content it needs, and any student-data constraints — ranking use cases by student impact and integration effort.
- Workflow audit
- Content & data map
- Prioritised use case
Build and validate the agent
We build against your real course content and student-data shapes, with an evaluation set scored against known-good answers before it reaches a student.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring
Production rollout into your LMS or support channel, with audit logging and a handoff so your academic and support teams can operate it independently.
- Production deployment
- Audit trail & monitoring
- Operator runbook
Not sure where to start?
A 30-minute call is usually enough to find your highest-value use case
Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own learner-support and course-content workflows. Every example is something already running in production.
Independence
How we help you stay independent
Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement.
Technological sovereignty
We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe.
Small language models
Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure.
Open source
We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source.
FAQ
Agentic AI in education, answered
What kind of institution is this actually for?
Training organizations, EdTech platforms, and universities with a workflow that already has a defined answer space — course content, student records, program guidelines, a knowledge base. We are not selling a generic AI tutor. We are building an agent around a specific workflow you already run manually or through rigid rules.
How is this different from the AI chatbot our LMS vendor already offers?
Most LMS chatbots retrieve a single document and paraphrase it. An agent plans across steps — checks a student's history, cross-references multiple sources, decides whether it has enough to answer or needs to escalate. Our multilingual chatbot for ARIJ Network reasons across a full training knowledge base in English and Arabic, not a single FAQ page.
What education workflows actually qualify for an agent, versus simple automation?
Anything with real reasoning across sources: student support answering questions against a knowledge base and student record, adaptive learning paths that adjust based on performance and multiple content sources, admissions or enrollment triage, and instructor-facing tools that draft feedback from rubrics and submission history. A single-lookup FAQ does not need an agent — a rules engine handles that fine.
Can an agent actually personalize learning, or is that marketing language?
An agent can read a learner's progress, prior responses, and stated goals, then select or sequence content accordingly — that is a real, buildable pattern. What it cannot do responsibly is grade high-stakes assessments or make enrollment decisions unsupervised. We draw that line explicitly during scoping, not after launch.
How do you handle student data and privacy?
We map data residency, retention, and access-control requirements before writing integration code. For institutions under FERPA, GDPR, or regional student-data regulations, the agent is scoped to only the fields it needs, with role-based access and a logged audit trail on every action it takes.
What happens when the agent does not know the answer?
It escalates rather than guesses. Confidence thresholds and gaps in the underlying knowledge base route to a human (instructor, support staff, or administrator) with the agent's reasoning attached, so the reviewer is not starting from zero.
Can this integrate with our existing LMS and student information system?
Yes, through existing APIs and data exports rather than a platform migration. The ARIJ Network deployment runs as a RAG-based chatbot layered on existing training content and delivery infrastructure across 22 countries, not a replacement system.
What is the realistic timeline and what have you actually shipped in this space?
A scoped Proof of Value on one workflow typically lands in about three weeks. On the ARIJ Network deployment, the agent moved inquiry response rate from near-zero to full coverage — 1% to 100% of inquiries answered, because the agent, not a human team, now handles first-line response across languages.
Start with one workflow
Map one student-facing workflow worth automating
A 30-minute call identifies your content sources, data constraints, and a realistic path to a working agent your academic team will trust.