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CASE STUDY / 01Learning intelligence · E Deviser

Cuevo.

I lead product direction and contribute to the architecture and engineering of Cuevo, E Deviser’s learning-intelligence product in development.

MY ROLE
Chief Product Officer & AI Product Engineer
PERIOD
2025–present
FOCUS
Agentic intelligence / Product engineering / RAG
Original conceptual illustration for Cuevo, E Deviser’s education product.
Project illustration
01 / THE CONTEXT

Learning connected through intelligence.

Cuevo brings curriculum, learning activity, assessment evidence and feedback into a connected experience for learners, educators and institutions. The product is being developed in English and Arabic, with support for right-to-left experiences. My work at E Deviser connects product direction with engineering and agentic intelligence, including evidence-grounded recommendations for what a learner or class should do next.

TOOLS & TECHNOLOGY
  • RAG workflowsScoped retrieval and relevant context

Retrieval and agentic intelligence form part of my product and engineering work on Cuevo at E Deviser.

02 / MY CONTRIBUTION

Product direction and agentic intelligence.

01

Product direction

I lead product strategy for Cuevo, shaping connected learning workflows and personalized next steps for learners, educators and institutions.

02

Connected education workflows

I contribute to architecture and development for the needs of educators, learners and institutions, with Arabic and English experiences.

03

Agentic intelligence

My work includes agentic and retrieval-augmented systems with controlled autonomy, alongside testing, security and observability practices.

03 / THE WORKFLOW

From context to meaningful action.

  1. 01
    STAGE 1 OF 5

    Understand the learning context

    Identify the learner or class, the curriculum and the question that needs attention.

  2. 02
    STAGE 2 OF 5

    Gather relevant evidence

    Bring permitted assessment, activity and feedback evidence into the request.

  3. 03
    STAGE 3 OF 5

    Reason about next steps

    Connect the evidence with a recommendation, rationale and uncertainty.

  4. 04
    STAGE 4 OF 5

    Review meaningful action

    Keep consequential decisions subject to educator or human approval.

  5. 05
    STAGE 5 OF 5

    Learn from feedback

    Use subsequent activity and feedback to inform the next learning decision.

PROJECT IN MOTIONLearning, in motion.
AI-generated concept animation of light moving through a glass book. A visual metaphor for learning, created with Sora 2.
04 / DESIGN CONSIDERATIONS

Personalization, control and trust.

Relevant evidence

A useful learning recommendation needs the right curriculum and learner context. Retrieval should preserve access boundaries and make the evidence understandable.

Educator control

A recommendation and authority to act are separate concerns. Consequential educational decisions need a clear human review boundary.

Clarity across languages

English and Arabic experiences need consistent meaning, readable right-to-left layouts and usable learning workflows.

Cuevo is the product; E Deviser is the company. The system sequence summarizes agentic learning workflows and is not a deployment diagram. The cover is original concept art; the considerations describe design tensions rather than claim particular released behavior.

NEXT PROJECT

AegisOps

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atta@edeviser.comBook a meeting with me

30-minute personal conversation · Google Meet.