Ask about education. Follow the evidence.
One chat interface. Evidence search works today; testing decisions is the next stage.
Ask in Arabic or English, continue the conversation, and open sources behind the answer. The current MVP does not calculate outcomes for a particular school.
School data connectors, causal and operational models, agent-based simulation, Monte Carlo uncertainty, and validation against observed outcomes are being built.
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Domain fine-tuning of Gemma 4 is under development. It is not answering this demo.
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Explore the source code and project notes on GitHub ↗.
The chat is the only control surface. An agent routes the question to the right backend path, keeps the assumptions visible, and explains the result.
The planner reads the latest question and short chat history, detects research, a school decision, or an unclear choice, and prepares a self-contained search query. Ambiguous operating decisions trigger a clarifying question.
Public documents are cleaned, deduplicated, split into passages, and indexed. BM25 and multilingual E5 find candidates; reciprocal rank fusion and a multilingual cross-encoder rerank them. The library currently holds 2,648 distinct works.
The answer model receives selected excerpts and metadata. The server checks cited IDs, quoted text, and numerical tokens against retrieved passages. This checks provenance, not whether every interpretation is causal or locally transferable.
Permissioned connectors will map school or university records into a baseline of students, teachers, rooms, timetables, budgets, attendance, and learning outcomes. Data quality, privacy, and missingness must be checked before modelling.
The agent will turn a decision into a defined intervention and constraints. Causal models estimate defensible effects; agent-based and operational models represent people, capacity, schedules, and costs; Monte Carlo runs express uncertainty.
Backtests and prospective pilots will compare predicted ranges with real outcomes and recalibrate the twin. The response will separate measured evidence, assumptions, simulated outcomes, subgroup impacts, resource needs, and uncertainty.
Checking the active model…
Fine-tuning and hosting Gemma 4 for this domain are under development. The language model will route and explain; it will not invent simulation results.
The status of each layer is explicit so a sourced answer is never confused with a tested prediction.
Arabic and English chat, automatic routing, retrieval over 2,648 distinct works, source inspection, citation checks, and an honest answer when evidence is insufficient.
Checking the current model…
Structured school and university connectors, baseline digital twins, causal estimates, operational constraints, agent-based simulation, Monte Carlo uncertainty, and evaluation against real outcomes. No school-specific forecast is available in this MVP.
Fine-tuning and GPU hosting for Gemma 4 are under development. The tuned model is not trained, hosted, or answering here yet.
Run historical backtests and prospective pilots, document where the models fail, calibrate uncertainty, and check equity and privacy before presenting institution-specific predictions as decision support.
Research, policy, and data you can trace back to the original.
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Source records may include versions or volumes of a work. Findings apply to the original publication's context.