Intermediate final year project ideas
Free intermediate final year project ideas on Sefism for CS and software engineering students, each scoped for a single semester with a suggested stack and deliverables.
- 300 project ideas
Ideas and guidance, not finished projects
These are project ideas and scoping guidance, published free for students to use as a starting point. I do not build, write, or sell final-year projects, and I do not complete coursework for anyone. Take an idea, make it yours, and build it.
160 ideas · page 1 of 7
Tamper-Resistant Dynamic Payment QR Display with Voice Confirmation for Small Shops
Printed payment QR stickers at small shops can be covered with a fraudster's sticker, and fake payment screenshots fool busy shopkeepers. Build a small counter device with a screen that shows a fresh, signed QR for each sale, and a speaker that announces confirmed payments only after the shop's own account receives them. Threat-model the attacks, build the device, and test it against sticker swaps and fake confirmations.
Tenancy Rights Guided Assistant with Rules as Code for Punjab and Lagos Rental Laws
Tenants in Lahore and Lagos often do not know whether a rent increase, an eviction notice or a withheld deposit is lawful, and lawyers are expensive. Build a guided web assistant that asks a tenant a few plain questions and explains, with the exact provisions, what the local tenancy law says about their situation, using rules encoded from the statute and reviewed by a law teacher. It generates a short letter template the tenant can adapt.
Privacy-First Legal Aid Intake and Case Routing for NGO Legal Clinics
NGO legal clinics and university law clinics in Pakistan, India, Nigeria and Kenya take requests by phone, walk-in and WhatsApp, and sensitive details about domestic abuse, labour disputes or police cases end up in personal phones and spreadsheets. Build a secure intake system with a guided form, issue classification into a standard legal taxonomy, routing to the right volunteer lawyer, and strict access control, encryption and audit logs. Threat-model it before writing code.
AI API Spend Attribution per Feature and Customer with Budget Guards
AI features have a cost per request that changes with prompt length, model choice and retries, and a single heavy customer or a bug in an agent loop can burn a month's budget in a day. Build a lightweight library and collector that tag every AI call with feature, customer and model, compute cost from token counts, show cost per feature and per customer, detect unusual spend quickly, and enforce budgets before the bill arrives.
Next 48-Hour PM2.5 Forecasts for South Asian Cities from Open Sensors and Weather Data
Each winter, smog in Lahore, Delhi, Dhaka and Kathmandu closes schools and harms health, and decisions are made with little warning. Build a model that forecasts PM2.5 for the next 48 hours at city sensor locations, using open sensor readings, weather forecasts and fire detections, and compare it against the global CAMS forecast. Present it as a simple outlook a school or clinic can use to plan.
Antenatal Follow-Up Prioritiser: Which Pregnant Women a Health Worker Should Call This Week
A health worker with 150 pregnant women on her list cannot call all of them every week. Build a mobile app and ranking model that suggests who to contact first, based on missed visits, risk factors and engagement history, and measures whether the ranking finds women who would otherwise drop out of antenatal care. The project is about fair, explainable prioritisation under a fixed call budget, not about diagnosing anyone.
TinyML Sound and Vibration Anomaly Detection for Backup Generators
Shops, clinics, mosques and offices rely on diesel or petrol backup generators during power cuts, and they fail at the worst moment. Build a small sensor node that listens to a generator's sound and vibration, learns its normal behaviour on the device itself with a tiny anomaly detection model, and sends an alert when something sounds wrong, without a cloud connection or a data scientist.
Which LLM Understands Roman Urdu and Bangla Business Messages? A Three-Model Evaluation Harness
Small businesses in Pakistan and Bangladesh get orders and complaints as WhatsApp-style messages in Roman Urdu, Urdu script, Bangla and English mixed together. Build an evaluation harness that compares three LLMs (for example one hosted frontier model, one cheaper hosted model and one open model) on real-shaped tasks such as extracting order details and classifying complaints, with a human-checked gold set, cost per correct answer and a public results page.
Pipeline That Turns Provincial Statistical PDFs into a Versioned Open Dataset
Governments publish important statistics as PDF yearbooks and reports: schools, hospitals, crops, prices and population by district. Build a data pipeline that extracts tables from these PDFs, cleans and checks them, tracks every change between editions, and publishes them as tidy, documented, versioned datasets that journalists, students and researchers can query directly.
District Medicine Stockout Early Warning from SMS Stock Reports at Rural Clinics
Rural clinics run out of essential medicines while a clinic 20 kilometres away has boxes about to expire. Build a system where clinic staff report stock on hand by structured SMS or a tiny web form, and a district dashboard forecasts which clinics will stock out in the next weeks and suggests transfers between clinics. The focus is the district view and forecasting from patchy reports, not a single pharmacy's ledger.
Mobile Money Agent Cash and E-Float Forecasting to Prevent Turned-Away Customers
Mobile money agents in Kenya, Pakistan, Bangladesh and Nigeria lose customers when they run out of cash or electronic float, especially on salary days and before holidays. Build forecasting models for each agent's cash and e-float needs from simulated transaction histories with realistic calendars, and a simple app that tells the agent and the aggregator when and how much to rebalance. Measure how many turned-away transactions the forecasts would prevent.
Offline AI Tutor Hotspot on a Raspberry Pi for Schools Without Internet
Many schools have a few phones or tablets but no reliable internet. Build a Raspberry Pi box that runs a small open language model and a curriculum-grounded retrieval system completely offline, creates its own Wi-Fi network, and lets students ask questions about their textbook in Urdu or English from any browser. Teachers control what content is loaded and see which topics students ask about most.
PII and Secret Leak Audit for LLM Chat Logs and Traces with Retention Rules
LLM apps log everything: prompts, retrieved chunks, tool results and answers. Those logs quietly fill up with phone numbers, CNIC numbers, addresses, API keys and other users' data. Build an auditor that scans existing LLM traces, finds personal data and secrets in English, Urdu and Roman Urdu, shows where each leak entered the pipeline, and enforces retention and masking rules going forward.
Government Notification Watcher with Plain-Language Summaries and Topic Alerts
Tax notifications, regulatory circulars and gazette notices change rules for small businesses and citizens overnight, but they are published as scanned PDFs on scattered government websites. Build a monitored pipeline that checks chosen official pages on a schedule, detects new documents, extracts text (with OCR when needed), writes a short plain-language summary with the source linked, and alerts subscribers by topic. Treat it as a reliability and cost engineering project.
Closed-Loop Patient Referral Tracking Between Village Health Workers and District Hospitals
When a village health worker refers a patient to a hospital, nobody usually finds out whether the patient arrived, what happened, or whether follow-up is needed. Build an offline-first referral app and a simple hospital desk screen that close the loop: referral slip with a short code, arrival check-in, outcome note and a follow-up task back to the worker. Measure how many referrals get an outcome recorded.
Glacial Lake Growth Monitoring from Sentinel-2 Imagery for Outburst Flood Risk in Northern Pakistan
Glacial lakes in Gilgit-Baltistan and Chitral are growing as glaciers melt, and some can burst and flood villages downstream. Build a pipeline that maps lake outlines from free Sentinel-2 satellite images every month, tracks area change over several years, flags lakes growing fast, and lists the settlements downstream. Validate the outlines against published inventories and hand digitising.
FOCUS Format Converter for Smaller Cloud Providers and AI API Bills
The FinOps Open Cost and Usage Specification (FOCUS) gives cloud bills a common format, and the big clouds now export it. Startups also pay smaller hosts, AI API providers and SaaS tools whose bills arrive in their own formats. Build converters that turn those bills into valid FOCUS data, a validator, and a dashboard that shows total cost and unit cost (per customer or per feature) across every provider in one place.
Tool Selection Evals and Description Linter for MCP Servers
An MCP server can work perfectly and still fail in practice because models pick the wrong tool or fill its arguments badly. Build a tool that lints MCP tool names, descriptions and schemas against good-practice rules, generates realistic user requests for each tool, measures how often several models choose the right tool with valid arguments, and suggests rewrites that improve the score.
MCP Server for Public Transport Timetables Built on GTFS Feeds
Build a Model Context Protocol server that lets any AI assistant answer real transport questions correctly: next departures, journeys with transfers, service changes and accessibility, all computed from GTFS timetable data rather than guessed by the model. Where a city has no public feed, the team builds one for a route they know, such as a university shuttle or a metro bus line, and publishes it openly.
Mandi Arrival and Price Forecasting with Sell-or-Store Advice for Farmers
Farmers often sell right after harvest when wholesale market (mandi) prices are lowest. Build forecasts of arrivals and prices for a few crops at nearby markets from public government data, and turn them into simple advice: sell now, or store for a few weeks, after storage costs and losses. Test the advice by replaying past seasons and measuring how often it would have helped.
Arabic Diacritics Reading Tutor with Automatic Tashkeel Feedback for Learners
Learners of Arabic, from schoolchildren in Pakistan and Nigeria to expatriate workers in the Gulf, struggle to read text without short vowel marks (tashkeel). Build a tutor that removes the marks from a passage, asks the learner to add them, and checks the answer against an automatic diacritiser and a verified reference, explaining errors by grammar rule. Measure diacritiser accuracy and whether learners improve.
AI System Inventory and Risk-Tier Register for Software Companies
Most companies cannot answer a simple question: where are we using AI? Build an inventory and risk register that discovers AI use (features in the product, AI tools staff sign into, AI APIs in code and cloud bills), records an owner and purpose for each, assigns a risk tier from a clear rubric mapped to the NIST AI RMF and the EU AI Act, and schedules reviews. Designed for software houses that sell to clients abroad.
Roman Urdu Jailbreak and Policy Drift Red Team for Customer Service Chatbots
Customer service chatbots are tested in English, then deployed to users who write in Urdu, Roman Urdu and a mix of all three. Build a red-team harness that checks whether a bot stays inside its own written rules (no promises it cannot keep, no advice outside its remit, no leaking its instructions) when attacked in local languages and code-switched text, and produces a scorecard the business can act on.
Crop Disease Photo Triage That Knows When to Ask an Agronomist, on Budget Android Phones
Most crop disease apps give a confident answer for every photo, even blurry ones, the wrong crop or a disease they never saw in training. Build an on-device classifier for a few crops that abstains when unsure and sends those photos to an agronomist review queue; the agronomist's answers become new training labels. The project measures how much accuracy improves when the app is allowed to say "I don't know".
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