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Evalezy is an AI evaluation API. You send it a question paper, a marking scheme and your candidates’ answers. It sends back question-wise marks, criterion-level reasons and feedback, plus an annotated PDF of each handwritten copy. Marks stay drafts until you finalize them. It is built for teams that already run exams and want the checking done faster:
  • School ERP vendors grading CBSE and state-board unit tests and pre-boards
  • University exam systems running on-screen marking of B.Com, BA and B.Sc answer booklets
  • UPSC and test-prep apps giving mains-style feedback on GS answers within hours of the test
Evalezy is built by the team behind Vacademy. Exams you create through the API also show up in your institute’s Vacademy dashboard, so teachers can review the AI’s marks there without you building a review screen.

Quickstart

Create an exam, submit an answer and read the AI marks in about ten minutes.

Authentication

Get an API key, choose its scopes and keep it safe.

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How it works

Every integration follows the same six steps.
1

Create an exam

POST /exams with the questions, maximum marks, correct options for objective questions and, for long answers, a rubric or model answer. Send "open": true to accept submissions straight away.
2

Add candidates

A candidate is identified by your own external_id (an admission or roll number). Register them up front, or send the candidate inline with each submission and Evalezy registers them for you.
3

Submit answers

For a handwritten exam, upload one PDF per answer copy and submit its upload_id. For a typed exam, send the answers as plain text in answers[].
4

Wait for grading

Submissions move through queued, processing, reading and grading, and end as graded, partially_graded, failed or cancelled. Poll GET /submissions/{id}, or poll the GET /submissions?updated_since= feed for many copies at once.
5

Read results

GET /submissions/{id}/result returns marks per question and per rubric criterion, the AI’s confidence, feedback, the text it read from the copy and a needs_review flag. GET /exams/{id}/results returns the same thing for a whole exam, page by page.
6

Finalize

POST /exams/{id}/finalize turns the draft marks into final marks. After that, marks can’t change unless you unfinalize the submission and give a reason.
The AI never publishes marks on its own. Every result is a draft until you call finalize. Your teachers, or your own rules, decide when marks are final.

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Two modes

Each exam has one mode, set when you create it.
Candidates write on paper. You scan each answer copy into one PDF (up to 50 MB) and upload it. Evalezy reads the handwriting, matches answers to questions, grades them and produces a checked copy: the same PDF with marks and remarks on it.
  • Price: 1 credit per page of the uploaded PDF, blank pages included.
  • Length: copies of up to 40 pages are graded normally. Copies of 41 to 80 pages are accepted but flagged for human review. Copies of more than 80 pages are refused.
  • Language: English answers only for now. A copy written in Hindi or another regional language fails with language_not_supported and is not charged.

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What you get back

For each question, a result has:

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What teachers see in the dashboard

Every exam you create through the API also appears in the institute’s Vacademy dashboard, tagged Source: API, and the exam response includes a dashboard_url that opens it. There, teachers can go through each submission, check the AI’s marks and feedback, and correct them where needed. Marks a teacher changes there show up in the API results, so teachers can review in the dashboard while your system still reads the marks through the API. If you would rather build review into your own product, the API has review endpoints too (scope evaluation:review).
It works one way only. API keys see exams created through the API. Exams that teachers create directly in the dashboard are not visible to API keys.

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Pricing in one minute

Evalezy uses a fixed price: you know what a copy costs before it is graded. Each submission response includes a quote, and credits_charged on the result matches it.
  • Handwritten: 1 credit per PDF page, blank pages included.
  • Typed: 1 credit per non-blank long answer. Objective-only submissions are free.
  • Failed, cancelled and unreadable copies are free.
  • Re-evaluating a copy is charged again.
Credits are bought in the Vacademy dashboard. Some institutes have a contract price; in that case the quote shows "rate_source": "contract". Full details are on the Pricing page; for current credit packs, see evalezy.com/pricing.

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Good to know

No. Every key is a live key and every graded copy uses real credits. To try things out, use a small exam with one or two questions and a short PDF. Objective-only typed submissions are free, so you can test exam setup and the submission flow without spending credits.
No. The API is server-to-server only. Anyone who has an API key can act for your institute, so it must never be shipped inside a web page or a mobile app. See Authentication.
Not yet. For now, poll GET /submissions?updated_since=<timestamp> to pick up every submission that changed since your last check. Webhooks are on the roadmap.
long_answer, mcq_single, mcq_multi, true_false, numeric and one_word. Long answers are graded by AI against your rubric. The other types are marked against the answer key.
Webhooks, phone photos as answer copies, creating an exam from a question-paper PDF, rubric generation on request and rubric locking, hosted review links, CSV results, Hindi and regional languages, and SDKs. See the roadmap.

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Base URL

All requests and responses are JSON, except file uploads and checked-copy downloads (PDF). Field names are snake_case. The machine-readable OpenAPI document is public at https://api.evalezy.com/v1/openapi.json.

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Next steps

Handwritten exams

Scan, upload and grade answer copies end to end, including checked copies.

Typed tests

Grade online tests that mix MCQs with long answers.

Answer-writing practice

Mains-style feedback on GS answers for test-prep apps.

Syncing results

Keep your system in step with polling, cursors and updated_since.

Rubrics

Write marking schemes the AI follows closely.

Going live

A checklist before you grade a real exam.