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Sannan Malik's avatar
Sannan Malik
United Arab Emirates
Design Expert | UI/UX Engineer | Lovable Expert
$1k+
Earned
2x
Hired
5.0
Rating
9
Followers
Design Expert | UI/UX Engineer | Lovable Expert
Cover image for Comprehensive Ticket Management System Development
Comprehensive Ticket Management System Development
1
7
Cover image for Banking App Landing Page Design
Banking App Landing Page Design
0
8
Cover image for Consultation Booking Interface for CyFocus
Consultation Booking Interface for CyFocus
1
11
Cover image for Figma to Loveable
Figma to Loveable
1
14
Capacitor(1)
Rahul Patel's avatar
Rahul Patel
Moncton, Canada
Web & Mobile App Developer for SaaS & AI-powered Products
5.0
Rating
5
Followers
Web & Mobile App Developer for SaaS & AI-powered Products
Cover image for VerticalWB VerticalWB is a mobile application
VerticalWB VerticalWB is a mobile application designed to help helicopter pilots quickly calculate aircraft weight and balance during flight planning. The app allows users to manage multiple helicopter profiles, input fuel and payload data, and visualize the aircraft’s center of gravity to ensure safe operating limits. I contributed to improving the mobile application by enhancing the user interface, optimizing performance, and implementing features that allow pilots to easily manage aircraft profiles and perform accurate calculations. Key contributions included: • Implementing helicopter profile management and customization • Improving mobile UI and user experience for aviation workflows • Integrating calculation logic for weight and center-of-gravity analysis • Optimizing performance for fast real-time calculations
1
177
Cover image for Full-stack SaaS MVP development for
Full-stack SaaS MVP development for startups, dashboards, APIs, automation, and mobile-ready products I built this SaaS MVP system for startups and business teams that need to launch faster, manage their work in one place, and avoid relying on too many disconnected tools. The platform includes a clean web dashboard, backend APIs, database structure, workflow automation, user activity tracking, and mobile-ready product screens. It helps teams manage customers, projects, leads, deals, reports, and daily operations from one central system. The dashboard gives business owners and teams a simple way to see what is happening across the product, including revenue, projects, contacts, pipeline activity, customer ratios, recent deals, and reports. This makes it easier to track progress, understand performance, and make faster decisions. I also built the backend workflow layer to connect APIs, webhooks, AI services, Google Sheets, and email actions. This helps automate repetitive tasks like lead updates, status changes, notifications, reports, and follow-ups, so teams can save time and reduce manual work. The mobile-ready experience shows how the same product can support users on the go, allowing them to track activity, view reports, manage records, and stay connected from their phones. This type of project is a good fit for SaaS founders, startup MVPs, CRM-style platforms, internal dashboards, client portals, marketplaces, workflow automation tools, and businesses that need a solid product foundation. My focus was to build something practical, clean, and scalable so the product could be launched, tested with real users, improved with feedback, and expanded over time.
1
71
Cover image for AI Voice Assistant Workflow for Call Automation I built a co...
AI Voice Assistant Workflow for Call Automation I built a complete AI voice assistant workflow using VAPI to help businesses handle calls faster, follow up with leads automatically, and reduce manual calling work. The system can be used for customer support, appointment booking, sales follow-ups, lead qualification, reminders, surveys, and technical support. Instead of relying on a person to answer every call or manually contact every lead, the AI assistant can respond, guide the conversation, collect information, and route calls when needed. The workflow includes AI assistant setup, phone number connection, inbound and outbound call configuration, call scripts, objection handling, transfer rules, testing, and performance tracking. This makes the system practical for real business use, not just a demo. The final setup gives businesses a scalable voice automation system that can improve response time, capture more leads, support customers more consistently, and track call performance through metrics like total calls, average call duration, and minute usage. This project shows my ability to build AI automation workflows that connect voice AI, business processes, and operational tracking into one complete system.
1
1
97
Cover image for OneSignal is a real-time messaging
OneSignal is a real-time messaging platform that uses APNs and backend APIs to deliver event-driven, stateful user updates across a lifecycle. It supports dynamic push notifications with personalized payloads, media, and timing controls, allowing systems to update user-facing content continuously rather than through one-time messages. The platform includes workflow automation (Journeys) to build multi-step, condition-based flows, where updates are triggered by events, delays, or user actions. It also supports scheduling and recurring triggers, enabling consistent state transitions over multi-day timelines. OneSignal integrates with backend services through APIs and webhooks, allowing external events (such as payments, registrations, or status changes) to trigger real-time updates. Combined with segmentation and user targeting, it ensures updates are delivered to the correct users with precise timing. This architecture directly aligns with Apple Wallet systems, where passes and Live Activities require continuous updates via APNs, driven by backend events and managed across a structured lifecycle (pre-event, active, post-event).
0
55
Capacitor(1)
Andrew J's avatar
Andrew J
London, UK
UI/UX Designer that loves creating immersive experiences.
45
Followers
UI/UX Designer that loves creating immersive experiences.
Cover image for Manifest: I AM - Gamified Goal Tracking App
Manifest: I AM - Gamified Goal Tracking App
0
21
Cover image for Zero Team AI Governance Single-Page Site
Zero Team AI Governance Single-Page Site
0
9
Cover image for Redesign of Damiano David's Landing Page
Redesign of Damiano David's Landing Page
2
76
Cover image for MuseoLens: Enhancing Museum Accessibility
MuseoLens: Enhancing Museum Accessibility
1
93
Capacitor(1)
Enoch Akhabue's avatar
Enoch Akhabue
Abuja, Nigeria
Full-Stack Engineer Delivering Secure and scalable Systems
Full-Stack Engineer Delivering Secure and scalable Systems
Cover image for -> Re-engineered the frontend architecture
-> Re-engineered the frontend architecture by upgrading Next.js and introducing TypeScript, improving application performance by 30% while strengthening type safety and maintainability. -> Integrated frontend features with backend APIs and authentication services, implementing protected routes, role-based access control, dynamic data flows, and API-driven functionality. -> Engineered 5+ core workforce management workflows covering staff scheduling, employee operations, client management, reporting, and administration for the platform. -> Contributed to the platform's PWA and mobile application experience using Capacitor, extending workforce management workflows from desktop web to mobile devices. -> Collaborated closely with backend engineers and product stakeholders to turn evolving requirements into functional product workflows, resolve integration issues, and continuously refine the user experience.
1
150
Cover image for Architected and delivered a semi-decentralized
Architected and delivered a semi-decentralized USDT ↔ KRW trading platform, balancing centralized compliance controls with decentralized transparency to enable secure, real-world crypto–fiat exchange in a regulated market. Co-designed a regulation-friendly transaction flow with product and compliance, enabling secure on-/off-ramp operations tailored to Korean financial regulations and reducing manual review overhead. Led multilingual localization (Korean/English), expanding regional adoption and driving a 40% increase in active users within the first year of launch. Optimized frontend performance and API design, cutting page load time by 35% and improving transaction completion rates through reduced latency and error handling. Owned end-to-end development across React, Node.js, and MongoDB, ensuring production-grade scalability and reliability for high-volume financial transactions with sub-second response times.
0
25
Cover image for WelliRecord - Co-Founder &
WelliRecord - Co-Founder & Full-Stack Systems Engineer Building patient-owned health data infrastructure designed for Africa. I lead the architecture and engineering of a health record platform that allows patients to securely carry their medical history across hospitals, reducing fragmented care and unnecessary repeated tests. Key systems I engineer: Patient-owned health data vaults Provider workflows for queues, triage, and clinical encounters Multi-organization access control for healthcare providers Secure and interoperable health data infrastructure I design and engineer reliable healthcare systems that keep patient data accessible even in low-connectivity environments, helping providers reduce fragmented records, repeated tests, and delays in care. Through strong system design, scalable architecture, and secure data infrastructure, I build solutions that connect healthcare workflows, improve data accessibility, and give organizations the technology they need to deliver better patient care.
1
48
Cover image for Finditor — Engineering in Practice Built
Finditor — Engineering in Practice Built Finditor to solve a simple but expensive problem: people and businesses have financial data, but often lack the systems to turn that data into clear decisions. As the Full-Stack Systems Engineer, I designed and built the product across the stack from frontend experience and backend architecture to data processing, APIs, database design, security, and infrastructure. The goal isn't just to make software that works. It's to build a system that turns complex financial data into actionable intelligence, while remaining reliable, scalable, and maintainable as usage grows. Finditor is a practical example of how I approach engineering: solve the business problem first, then build the system that can support the solution.
1
48
Capacitor(1)
Durgesh Kumar's avatar
Durgesh Kumar
Kharagpur, India
Full-stack developer and founder building real-world product
New to Contra
Full-stack developer and founder building real-world product
Cover image for During my internship at Neosophical
During my internship at Neosophical Labs, I single-handedly built Sabse Bada Fan (https://sbf.ahoum.com/)(SBF), a live multiplayer quiz game, taking it entirely from an initial idea to a fully launched application. I designed the system to handle over thousands of people playing at the exact same time without slowing down, and I made sure the live leaderboards update instantly for everyone. To make the game accessible everywhere, I integrated Capacitor to seamlessly package the web platform into native Android and iOS mobile apps. Finally, I built an automated background system that perfectly runs our scheduled live quizzes and handles all the scoring completely on its own without any human effort.
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29
Cover image for Built for the Accenture Innovation
Built for the Accenture Innovation Challenge (Round 2), this one tackles a problem most companies rolling out AI don't think about until it bites them: if you're running several different LLM-powered features at once — a customer support bot, an internal knowledge assistant, a tool inside a regulated decision workflow — each one needs a completely different level of scrutiny. A support bot can tolerate more risk and needs to respond fast; a regulated decision-support tool needs to be slow, careful, and willing to hold a response for human review rather than guess. AuRizz is a middleware layer that sits between an enterprise's LLM provider and its users and decides, per use case and in real time, whether a generated response should go through as-is, get redacted, get held for a human, or get blocked outright. What made this a genuinely hard build, not just an API wrapper: Policy-driven, not hardcoded. Every behavioral difference between use cases — which checks run, their thresholds, what happens on failure — lives in YAML config, not in application code. Switching a deployment's risk posture is a config edit, not a redeploy. Confidence as a first-class signal. Every check returns both a risk score and a confidence in that score — a high-risk-but-unsure reading routes to human review instead of auto-blocking, which is the actual fix for alert fatigue. Multi-provider by design. Works with Anthropic, OpenAI, Gemini, or Groq behind one interface, with automatic runtime fallback if a provider fails — switching providers is a one-line config change, not a rewrite. A real audit trail. Every decision is written to a hash-chained, tamper-evident log, and human overrides are appended as new records rather than edits — so the fact that the system said block and a human said allow is preserved, not erased. Built to be tested. 104 tests run the full pipeline — detectors, aggregation, decision rules, the audit chain — with no network calls or API keys required, because everything is built against interfaces, not vendor SDKs directly. I worked on this with a teammate — I focused on architecture, pipeline, backend, and we split the detector logic, policy engine, and audit system between us. Stack: Python, FastAPI, React, TypeScript, Pydantic, SQLite, multi-provider LLM integration (Anthropic/OpenAI/Gemini/Groq) Status: this is a working prototype, not a deployed product — the source is public and the full pipeline runs locally with real API calls or offline in the test suite, but it hasn't been hosted or put in front of real traffic yet. If you're building anything where AI output needs a guardrail — content moderation, compliance-sensitive automation, multi-agent systems that need oversight — this is exactly the kind of system design problem I enjoy.
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22
Cover image for I built this because I
I built this because I was annoyed. I was paying monthly for a study library seat, and I still couldn't reliably tell if a seat would be free when I showed up. No way to check online, no way to book ahead just calling around or walking in and hoping. I figured if this was frustrating me, it was frustrating thousands of students across India who rent library/reading-room seats the same way. So I built Studoo (https://studoo.aithro.in/)and Padhloo (https://padhloo.aithro.in/)two connected products at Aithro (https://aithro.in/products)- Padhloo (student side): search study spaces by city/price/amenities, see a live seat map, request a specific seat for 1-12 months, and track your bookings, dues, and payments in one place. It also gamifies actually showing up — QR check-in/out, study streaks, points, badges, a leaderboard — so students stay consistent, not just book-and-forget. Studoo (owner side): the dashboard library/reading-room owners use to list their space, manage seat inventory and pricing per slot, approve or reject booking requests, chase due payments automatically, and see real analytics (occupancy, revenue, which slots to reprice). Free tier + two paid plans. You can see product demo video here - https://youtu.be/9Yex73MIYqQ?si=jfWqLxisRTM9N_28 I designed and built the full product end-to-end — the booking/approval workflow, the seat-and-slot inventory model, the QR-based geofenced attendance system, the payments/dues tracking, the subscription tiers, and the whole gamification layer (points, streaks, badges) on the student side. Where it stands: Got our first paying customer, a real library owner in Patna paying for the Growth plan, which is the moment this stopped being a side project and started being a business. Reached the Grand Finale of Empresario 2026 (IIT Kharagpur E-Cell), selected out of 3,000+ global registrations. Won the Platinum Jubilee Innovation Challenge (Deep Tech) at IIT Kharagpur's 75th anniversary event. What I'm good at, based on building this: turning a messy real-world workflow (owner approves seat → student pays offline → both sides need to trust the same numbers) into a system that's simple enough for a small business owner with no tech background to actually use — while still handling the edge cases (partial payments, overlapping slots, expiring bookings, geofenced check-ins) correctly under the hood. If you're a small business or founder who needs a web app or an AI-automation workflow built and shipped fast, not just prototyped that's exactly the kind of problem I like solving. Happy to talk about your project.
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Cover image for SpeakYield - voice-first agri-commerce platform
SpeakYield - voice-first agri-commerce platform for Indian farmers This one came out of a government hackathon problem statement, but the problem itself is real and ugly: a smallholder farmer in Maharashtra selling onions knows yesterday's price at exactly one mandi, usually from the same commission agent who's also buying from them. They can't see the market 40km away, can't see what a processor or exporter would pay, and often sell right after harvest at the seasonal low because they need cash now, not because it's the best time to sell. Every step of that chain - price, buyer trust, quality grading, transport, payment - quietly leaks money out of the farmer's pocket. I built SpeakYield to close that gap, and built it voice-first because the people this is for often can't or won't use a text-heavy app. A farmer can literally say "I want to sell 12 quintals of onion, I need ₹40,000 right now" in Marathi/Hindi, and the app: Parses that into a structured request and reads it back for confirmation before anything commits - nothing moves on a guess Shows every nearby buyer's price as net-in-hand, after transport, commission, and fees - not the misleading headline mandi rate Recommends sell-now / split / hold based on the farmer's actual cash need, not just a price chart Ranks competing buyer offers by what the farmer actually banks, weighted by the buyer's payment-reliability history Runs the full deal lifecycle — offer, logistics, milestone payments, dispute filing — on one transparent, tamper-evident record What I built: the full stack — Next.js/TypeScript frontend, FastAPI backend, the voice pipeline (speech-to-text → LLM intent parsing with strict schema validation → text-to-speech read-back), the buyer-matching and net-price ranking engine, and the deal/ledger system. Solo project, ~12 commits from architecture to a working two-sided prototype (farmer view + buyer view), with CI, linting, and end-to-end tests. Stack: Next.js, TypeScript, FastAPI, Python, LLM-based voice intent parsing, PostgreSQL/Redis (production design) I like this kind of problem specifically because it's not "build a CRUD app" — it's translating a messy, high-stakes, low-trust real-world transaction into software that has to work for someone with a ₹6,000 phone and patchy network, not a power user. If you need someone who can take a genuinely fuzzy problem (multi-party trust, real-time matching, voice/AI interfaces) and turn it into a working product fast, that's exactly this.
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Capacitor(1)