Sikgen AI

Civil-services coaching institute · Tamil Nadu, India

Unique IAS Academy

A civil-services coaching institute running AI tutoring, generated practice and proctored mock exams on its own study material in Tamil and English.

The problem

Civil-services preparation runs over years rather than months, so the material an institute accumulates — notes, current-affairs compilations, previous years’ papers — becomes both its main asset and an archive too large for any student to navigate unaided.

Question-paper setting is the constraint on how often an institute can test. Producing a full-length paper by hand is a senior faculty task, which caps mock frequency well below what candidates need.

Aspirants sit the real examination in Tamil or English, so practice that is only available in one of those languages does not prepare them for the paper they will actually face.

How the deployment works

Institutional material becomes the answer source

Study material is uploaded as PDFs and documents, parsed, split into passages and embedded into a vector index scoped to the institute. When a student asks a question, the relevant passages are retrieved and passed to a language model as the context for that answer, which is why the answer can cite the page it came from. Faculty can verify what the tutor said rather than take it on trust.

Papers drafted from previous years’ questions

Past papers and notes are used to draft new items in the institute’s own format. Generation is anchored to a specific source passage, so a reviewer reads the passage and the question together rather than verifying a claim from memory. Nothing reaches a candidate until a subject expert has approved it — the workflow assumes a human in the loop.

Mocks that mirror the real interface

Full-length papers run under timing and fullscreen-proctoring conditions built to match the examination interface candidates will sit, in Tamil or English. Practising in the format is part of the preparation, not a detail.

Analytics at topic level, not just totals

After each attempt the platform reports per-candidate weak topics, pace against the clock, and cohort-level segmentation — so faculty intervention can be aimed at a specific topic and a specific group rather than at the whole batch.

Results

We are not publishing outcome figures for this deployment. A number is only meaningful alongside its definition and the period it was measured over, and we would rather show you nothing than show you something you cannot audit. If a specific metric matters to your evaluation, ask on the call — we will tell you what we can and cannot substantiate.

See it on your own content

Bring your syllabus and a real batch. We will show you what the platform does with them.

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