Recruiters see the same projects again and again: a to-do app, a weather app, a Netflix clone. They do not prove much, and in 2026 they do not stand out.
A strong portfolio project solves a real problem, is deployed, and includes at least one AI feature that is genuinely useful, not a chatbot bolted on for the sake of it. Here are twelve ideas, grouped by difficulty, with what each one proves.
Suggested stack for all of them: Next.js (React + TypeScript), Node.js API routes, PostgreSQL with Prisma, an LLM API, deployed with Docker or a cloud platform. Swap in Spring Boot if you are on the Java track.
Beginner (after you know React and a database)
1. Smart expense tracker
What it does: users log expenses; the app suggests a category for each entry automatically and gives a monthly summary in plain English. AI feature: classify each expense description into a category with structured output; generate a short monthly insight ("You spent 30% more on food delivery than last month"). Proves: CRUD, auth, charts, structured LLM output, handling wrong AI suggestions (users can correct the category).
2. Resume and job-description matcher
What it does: paste a job description and upload a resume; get a match score and specific suggestions. AI feature: extract required skills from both, compare them, and explain the gaps. Proves: file upload and parsing, prompt design, presenting AI output clearly. Bonus: you will use it yourself.
3. Recipe planner from what's in your kitchen
What it does: list ingredients and dietary needs (vegetarian, Jain, diabetic-friendly); get a weekly meal plan and a shopping list. AI feature: constrained generation with structured JSON output that your UI renders as cards. Proves: validating AI output against rules (no onion in a Jain recipe), good UX for AI features.
4. Study notes summariser
What it does: upload lecture notes or a PDF; get a summary, key terms and practice questions. AI feature: summarisation and question generation, with links back to the source page. Proves: PDF processing, streaming responses, citing sources.
Intermediate
5. Policy and FAQ assistant for a business (RAG)
What it does: a business uploads its policies and FAQs; customers ask questions and get answers with citations, or a polite "I don't know". AI feature: retrieval-augmented generation with a vector database (pgvector works inside PostgreSQL). Proves: embeddings, chunking, retrieval, citations, refusing to guess. This is one of the most-requested AI skills in industry. Learn the concepts in What is RAG?
6. Invoice and bill data extractor
What it does: upload photos or PDFs of invoices; the app extracts vendor, date, GSTIN, line items and totals into a table, and flags totals that do not add up. AI feature: document understanding with a vision-capable model plus schema validation. Proves: multimodal AI, validation in code, a human review screen for low-confidence fields. Very relevant for Indian small businesses.
7. Semantic product search for an online store
What it does: shoppers search in natural language ("cotton kurta for summer office wear under ₹1,500") and get relevant results even when the words do not match. AI feature: embeddings for semantic search combined with regular filters for price and size (hybrid search). Proves: search relevance, combining AI with traditional queries, performance.
8. Support ticket triage dashboard
What it does: incoming tickets are automatically categorised, prioritised and routed, with a suggested reply for an agent to edit. AI feature: classification, summarisation and draft replies, with a human always approving the send. Proves: human-in-the-loop design, admin dashboards, role-based access.
Advanced
9. AI agent for a small business with approval steps
What it does: an assistant that can look up orders, check stock and draft a refund, but must get a manager's approval before acting. AI feature: a tool-using agent (LangGraph or a plain tool loop) with an approval interrupt. Proves: agent design, safe tools, approval flows. Follow our LangGraph tutorial to get started.
10. Meeting notes to action items
What it does: upload a meeting recording; get a transcript, a summary, decisions and action items assigned to people, pushed to a task board. AI feature: speech-to-text, extraction into structured tasks, integration with a task API. Proves: async processing with background jobs, integrating multiple AI steps, real-world integrations.
11. Code review assistant for GitHub pull requests
What it does: when a pull request opens, the app reviews the diff and posts comments on possible bugs and missing tests. AI feature: reasoning over code diffs, with comments posted via the GitHub API. Proves: webhooks, GitHub integration, prompt design for precise feedback, and handling false positives.
12. Multilingual government-scheme helper
What it does: users describe their situation in English, Tamil, Hindi or another Indian language; the app explains which public schemes they may be eligible for, with links to official sources. AI feature: multilingual RAG over official scheme documents, with clear disclaimers. Proves: multilingual AI, RAG on public data, responsible design for a sensitive use case.
How to make any of these portfolio-worthy
The idea is only the start. What impresses reviewers is the engineering around the AI:
- Deploy it, with a live link in your README
- Keep secrets on the server: never call the LLM API from the browser with your key
- Handle failure: timeouts, rate limits, bad output, empty results
- Validate AI output in code before using it
- Control cost: limit input size, cache repeated requests
- Measure quality: a small test set of 20 inputs with expected outputs, and how often you get them right
- Write it up: architecture diagram, key decisions, what went wrong and how you fixed it
One project with all of this beats five without it.
Which one should you start with?
- Freshers: start with 1, 2 or 4, then build 5 (RAG) as your standout project
- Applying to product companies: 5, 7 or 9
- Interested in AI engineering: 5, 9 and 11, then read our agentic AI roadmap
For the full skill path behind these projects, see the full stack developer roadmap for 2026.
Build your portfolio with mentors and code reviews. In our AI-Enabled Full Stack Developer Program, every project includes an AI feature, and the program ends with a team product sprint and a deployed capstone. Six months, live online, from anywhere in India.