Noetis Education · Business engine
Source-Based Learning Content Engine
Creates source aligned learning materials.
Answers: "Can an engine create learning material from our approved sources only?" For educators and training teams.
Uses only the source pack you approve. It does not claim learning outcomes.
Available today
ImplementedImplemented: runs on the shared runtime with supplied material. One bounded development check on 2026-10-12 took 126.7s with 13 model calls and ended "incomplete". That is a development signal, not an evaluation. Customer access is by approved account.
What you send
- Question
- Supplied text material
- Text material up to 150,000 characters per run
What comes back
- Lesson or study package with verified answer keys.
- Findings with the located quote and a support status from claim review
- Release status: released, incomplete with reasons, or failed
- Executed roles, usage and measured cost
Sample
Authored illustration. Written by Encevra to show the workflow. Not customer data and not a recorded model output.
Create a lesson on simple interest for 14 year olds.
Source: simple interest I = P × r × t. Example: $500 at 4% for 3 years gives $60. Common error noted by teachers: using percent as a whole number (4 instead of 0.04).
- Material drafted from the supplied curriculum, with the source passage for each point
- Answer checks shown against the source
- Gaps where the source pack does not cover an objective
The real response shape is documented in the API reference.
How the workflow operates
Each role is a responsibility in the workflow, served by a third party language model. More roles are not automatically better; depth and routing decide which run.
Models this engine uses: OpenAI (GPT), Anthropic (Claude). Specialists run on OpenAI models and the claim review and final brief on Anthropic models. If a provider has a temporary failure, the step can fall back to an OpenAI model, and the run records that.
Curriculum Mapper
Identify objectives and prerequisite knowledge.
Explanation Designer
Develop accurate explanations and worked examples.
Misconception Analyst
Anticipate likely misunderstandings.
Assessment Reviewer
Check practice questions, solutions and objective coverage.
Evidence and deployment
Evidence
This engine reads the text you send in the request. Each released finding carries the exact quote and its character span in that text, located mechanically before release. Quotes that cannot be located are labelled Insufficient evidence and never shown as source verified. Interpretation is reviewed as a separate step, and gaps such as a missing clause describe what was searched instead of citing text that is not there.
Deployment
Runs on Encevra hosted infrastructure using the third party models listed above. Every model call and fallback is checked against the deployment policy first. Private cloud and air-gapped deployment are planned; discuss deployment requirements.
What it does not handle
- Grading students
- Uses only supplied material. No freshness guarantee beyond the documents provided.
- Required for every output during trials.
How to integrate
Send "engine": "education" with your question and material from your server. Keys stay server side.
How to evaluate it
No published result for this workflow yet. Build a small answer key from your own work and compare against a strong single model baseline. See the evaluation checklist and method.
Cost and rights: pricing and licensing. Showing outputs to your own customers needs a written agreement.
Related engines
- Learning Coach Engine for Education Apps
- Research Synthesis API for Supplied Documents
- Writing and Communication Engine for Apps
Content reviewed 2026-10-12 by the Encevra engine team. Engine version 0.1.0 on noetis-core-1.


