How much does it cost to build a AI study app that reorganises course PDFs in India (2026)?
Short answer: ₹4,51,000 – ₹5,93,000 for an MVP, over 7–10 weeks. The slip below shows the team, the phases, and — the part most quotes hide — what gets cut to reach that price.
We priced this page with the same estimator that prices your idea, from the brief below — not a hand-written price list, and not a quotation. Your build moves the moment your requirements do.
Estimate · not a promise
A mobile/web app where a student uploads a course PDF and gets back an AI-structured learning path with summaries, reordered sections, and auto-generated practice questions.
From: An app where a student uploads a course PDF and it is reorganised into a structured learning path, with summaries, sections in a sensible order, and practice questions generated from the material.
Total to build an MVP
₹4,51,000 to ₹5,93,000
7–10 weeks · medium confidence
The team you'd need
Full-stack engineer (backend AI pipeline + web frontend)
senior · 2 months₹3,60,000–₹4,80,000
UI/UX designer (part-time)
mid · 0.5 months₹35,000–₹50,000
QA (part-time)
junior · 0.5 months₹16,000–₹23,000
Tools & infrastructure₹40,000
How the weeks are spent
- 1Design & architecture — PDF ingestion flow, UI wireframes, LLM prompt design1w
- 2Core pipeline — PDF parsing, chunking, LLM structuring, section ordering, summary generation3w
- 3Practice questions + frontend — Q&A generation, learning path UI, upload flow2w
- 4QA, edge cases, polish, deploy1w
Not in this MVP5
To add later
User accounts and progress tracking+₹80,000 · 3w
Core value is the PDF-to-path transformation, not persistence; test with stateless sessions first
Spaced repetition / flashcard scheduler+₹1,20,000 · 4w
Adds algorithmic complexity not needed to validate question quality
Multi-format upload (DOCX, slides, video transcript)+₹60,000 · 2w
PDF covers the dominant student use case; other parsers are separate integrations
Collaborative or shared learning paths+₹1,50,000 · 5w
Single-user flow must be validated before social layer adds coordination overhead
Native mobile app (iOS/Android)+₹2,00,000 · 6w
Responsive web MVP is sufficient to test the core loop without dual-platform build cost
Priced assuming5
LLM API (OpenAI/Gemini) handles structuring and Q&A generation without fine-tuning
If that's wrong: Fine-tuning or RAG pipeline adds 3–4 weeks and ₹80–120K
PDFs are text-based (not scanned images); no OCR pipeline needed
If that's wrong: Adding OCR via Textract or similar adds ₹20–30K and 1 week
LLM API costs during MVP period stay within tools budget (low user volume)
If that's wrong: High-volume testing could add ₹15–40K in API overage
No user authentication required for MVP — anonymous upload and view
If that's wrong: Auth layer adds 1 week and ₹30–50K
Single engineer can own full stack including LLM prompt engineering
If that's wrong: If prompt quality is poor, an ML specialist adds ₹1.6–2.4L/mo for 1 month
Price rises if3
Impact
LLM output quality is inconsistent across PDF types (dense academic vs. casual notes), requiring extensive prompt iteration+₹60,000–₹1,20,000 · 3w
PDF parsing fails on complex layouts (multi-column, tables, footnotes), requiring a more robust extraction library or manual fallback+₹30,000–₹60,000 · 2w
Founder requests user accounts and saved paths mid-build, expanding scope+₹70,000–₹1,00,000 · 3w
Priced by Foundco · estimator-in.netlify.app
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