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Modern, AI-assisted querying for education data

Turn complex education data into answers — without writing any code.

Query Forge is a modern, AI-assisted query platform built for teams working with complex, highly-structured education data — especially Ed-Fi and CEDS.

Query Studio guides users with schema awareness, domain knowledge, and living data dictionaries — while AI assists with suggestions and explanations so analysis stays fast, consistent, and defensible.

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No guesswork. No broken joins. No “what does this column mean?” Just answers you can trust.
Query Studio overview

The Challenge

Ed-Fi and CEDS data models are deeply normalized, relationship-heavy, and easy to misuse without context. Teams lose time on schema trivia, definition drift, and fragile SQL.

  • Understanding how tables relate
  • Time-bound enrollment and student logic
  • Consistent metric definitions
  • Defensible reporting and explainability
What Query Forge unlocks
Speed
Guided construction reduces trial-and-error and review cycles.
Trust
Transparent logic + definitions make metrics repeatable.
Clarity
Data dictionary context stays embedded in analysis.
Control
AI assists without hiding the work.
Build a Query Explore the Data Dictionary

Introducing Query Studio

Query Studio is the visual, AI-assisted workspace where users build accurate, explainable queries without writing SQL by hand.

How it feels
  • Start with intent, not tables
  • Valid join paths only (guardrails)
  • Measures and filters aligned to definitions
  • Explainability built in
See Query Studio See How AI Assists
Query Studio canvas

Powered by living data dictionaries

Query Forge treats data dictionaries as first-class citizens. Column definitions, business rules, and usage context stay visible while users build — reducing definition drift and onboarding time.

View Dictionary UX Education Examples
Data Dictionary panel

AI that assists — not obscures

Query Forge uses AI to accelerate analysis while keeping every decision transparent, reviewable, and under your control. It’s not “magic” — it’s a guided workflow that understands your model, recommends safe structures, and explains the logic.

What AI does for you
Understands intent
Parses questions into entities, measures, and constraints.
Grounds recommendations
Uses your catalog + dictionary to suggest the right fields safely.
Applies guardrails
Education-aware logic: time bounds, enrollment rules, and more.
Explains “why”
Plain-language explanations of joins, filters, and assumptions.
Built for accuracy-first workflows: grounded retrieval, explicit assumptions, and human-in-the-loop review — especially critical for Ed-Fi and CEDS reporting.
How the AI works
Agentic • Grounded • Explainable • Private-by-design
1
Intent extraction (NER)
Education-aware NER extracts entities, measures, and constraints from the user’s prompt.
  • Programs, subgroups, grades, schools
  • Measures like attendance, enrollment, outcomes
  • Time windows, statuses, cohort logic
2
Grounding via vector search
The extracted intent queries private vector indexes built from your domains, tables, columns, and data dictionary definitions.
This keeps suggestions grounded in your actual model — not generic guesses.
3
Agentic reasoning + guardrails (Llama)
A Llama-powered reasoning layer proposes query structure (joins, filters, groupings) and produces a plain-language explanation.
AI can recommend and explain — but it cannot silently execute or hide decisions.
4
Data isolation & privacy by design
Your data never leaves your environment. We do not send schemas, records, or queries to public cloud AI services.
  • No external LLM APIs (ChatGPT, Claude, Gemini, etc.)
  • No training on your data — ever
  • Reasoning runs against your private metadata
5
Review, refine, and reuse
Users review suggestions and assumptions, accept/edit/reject, then save reusable query templates for consistent reporting across teams.
Explore Query Studio View the Data Dictionary
Private-by-design AI • Grounded retrieval • Explainable logic • Human-in-the-loop by default

Built for Ed-Fi & CEDS (by design)

Query Forge is purpose-built for education data: student-centric models, time-bound enrollment logic, attendance, discipline, programs, and definition-sensitive reporting.

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Ed-Fi and CEDS examples
Ready to Forge Better Questions? Fast • Defensible • Explainable

Forge Better Questions

Education data deserves more than trial-and-error querying. Query Forge helps teams ask better questions, understand their data, and trust their results — without sacrificing transparency or control.

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