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SmartBite, a University of Central Punjab final-year project, accepted food orders without integrating a payment gateway: customers sent the exact amount to the project’s merchant bank account by Raast or bank transfer, uploaded a receipt, and waited for an administrator to approve the payment. That kept checkout working for the team’s demo, but traded automated confirmation for a slower manual review.
Huzaifa Iftikhar, a Lahore-based software engineer and member of the student team, describes the project as a marketplace for home chefs, nearby customers, delivery riders, and ingredient vendors. Its experience is useful as a specific engineering case study—not as evidence that payment gateways are generally unavailable to businesses in Pakistan.
How SmartBite handled payment without a gateway
The team first included JazzCash as an option in an enum, then removed that plan. Instead, its checkout showed the customer a merchant bank account IBAN and a QR code for the order’s exact amount. The customer opened their own banking app and sent a Raast or bank transfer. They then uploaded a screenshot or transaction receipt in SmartBite.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems- Checkout: SmartBite displayed the amount due, the project’s merchant IBAN, and a QR code for that amount.
- Transfer: The customer initiated a Raast or bank transfer from their banking app.
- Proof: The customer uploaded a screenshot or transaction receipt to the order.
- Review: An administrator checked the transfer against the bank account and approved or rejected it.
- Order progression: The order advanced only after approval.
This arrangement avoided gateway API keys and monthly fees, and sidestepped a merchant onboarding process the team says it could not complete for its project. Those are the team’s stated constraints, not a finding about gateway availability or eligibility across Pakistan.
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What the manual payment flow changed
A gateway can automate payment confirmation within an integrated checkout. SmartBite instead put a person between the transfer and order progression. That made transfer verification possible without a gateway integration, but slowed the process: Iftikhar acknowledges that manual review is slower than card payment.
| Dimension | SmartBite’s described transfer flow | Gateway-integrated checkout |
|---|---|---|
| Payment initiation | Customer leaves the food app and transfers through their banking app. | Payment is initiated through the integrated checkout. |
| Confirmation | Customer uploads proof; an administrator checks the bank account and decides. | Confirmation can be automated through the integration. |
| Order timing | The order advances after manual approval. | Order progression can follow the integration’s confirmation response. |
| Known trade-off in this account | Manual review is slower; the article reports no measured review time, conversion rate, fraud rate, or cost comparison. | The project account does not provide a measured comparison. |
The project description does not establish the business registration, merchant eligibility, legal, accounting, fraud-control, or consumer-protection requirements that would apply to a real commercial marketplace. A demo workflow should not be treated as evidence of commercial compliance.
What the team built around checkout
SmartBite was designed as a marketplace: home chefs prepare meals, nearby customers order them, riders deliver them, vendors supply ingredients, and administrators oversee the platform. Iftikhar describes five applications sharing a backend:
- Backend: An Express 5 and TypeScript server using Mongoose, MongoDB, and Socket.IO.
- Operations dashboard: A Next.js 15 web app for chefs, vendors, and administrators.
- Customer and rider app: One Expo SDK 54 and React Native 0.81 app, with route groups separating the customer and rider experiences.
- Recommendation service: A Python 3.11 FastAPI service using scikit-learn.
- Public site: A Next.js landing page.
The project used a shared users collection across web and mobile, so an account created in one frontend could also sign in through the other. Keeping customer and rider flows in one mobile project also reduced the number of codebases the small team had to maintain.
How orders moved from kitchen to customer
- A chef marked an order ready for pickup.
- The dispatcher offered the delivery job to nearby riders.
- A rider accepted, attaching themselves to the order.
- The rider’s phone sent GPS position updates.
- The customer app polled every 15 seconds for the order status, kitchen location, and latest rider position, then showed the rider’s moving map pin.
Iftikhar says the team tested this flow with live requests against a running server. Walking through it as a user exposed a stale active-delivery screen for riders after an order’s status changed. As he put it: “A system can be completely correct and still be broken for the person using it.”
Fallbacks that kept the demo usable
The system had alternatives when supporting services were unavailable. If the recommendation service was down, the backend fell back to popularity ranking. If the map integration was unavailable, the project could use straight-line distance instead. Iftikhar presents these fallbacks as ways for a demo to keep working without paid API keys or reliable internet; they describe the design, not independently measured uptime.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How recommendations worked with little marketplace history
A new marketplace has little customer rating history to learn from. SmartBite’s described live approach therefore combined text similarity and popularity: it represented each meal’s name, description, and tags with TF-IDF, compared meals using cosine similarity, and ranked meals without user history using a Bayesian average rather than raw ratings. Recommendations also included a plain-language reason.
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The team separately experimented with collaborative filtering using a public Food.com dataset from Kaggle. Iftikhar reports filtering that dataset to 150,000 interactions, splitting 120,000 for training and 30,000 for testing, and training an SVD model with 100 factors over 20 epochs. He reports 14,518 users and 14,972 recipes in the experiment, with held-out RMSE of 0.936, MAE of 0.536, and Precision@10 of 0.913 using a relevance threshold of 4.0. These are the author’s reported results; they are not independently reproduced here. The Food.com experiment does not establish recommendation quality for SmartBite’s own customers.
What this project does—and does not—show
SmartBite shows how one student team assembled ordering, manual bank-transfer verification, dispatch, location updates, and recommendations into a working project concept. Its payment flow illustrates a concrete alternative when the team could not complete gateway onboarding: accept a customer-initiated transfer, collect proof, and have an administrator verify it.
It does not establish that Pakistani food-delivery businesses generally lack gateway access, that manual transfer review is faster or cheaper in production, or that the described process meets the requirements for operating a commercial marketplace. Those questions need evidence beyond this project account.
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