Freelancers using Google Gemini in BengaluruFreelancers using Google Gemini in Bengaluru
ProjectsPeople
Mansi Singh's avatar
Mansi Singh
proBengaluru, India
AI Video Producer | AI Campaigns & Workflows
$5k+
Earned
4x
Hired
5.0
Rating
24
Followers
AI Video Producer | AI Campaigns & Workflows
Cover image for Menswear AI Editorial —Ralph Lauren
Menswear AI Editorial —Ralph Lauren Spec Campaign A self-initiated spec project exploring what AI-generated menswear imagery can look like when the brief is treated seriously. Sharp model direction, intentional lighting, and fabric detail that holds up at hero image scale. Three shots across two setups: studio portrait and lifestyle. Built entirely using AI and using detailed scene and lighting direction. No post-production filters. The goal was simple, make something a real brand could drop on their website tomorrow. This is the gap I close.
0
354
Cover image for Editorial-Quality Interior Images
Editorial-Quality Interior Images
1
7
Cover image for DUST Identity: AI Brand Film
DUST Identity: AI Brand Film
1
18
Cover image for Le Secret, a spec film
Le Secret, a spec film for Chanel Rouge Allure Laque, shade 83 Beige Secret. One shade, three skin tones, one warm rosewood world. Made entirely made with AI. *Spec work. Not affiliated with or commissioned by Chanel.
2
2
246
Google Gemini(2)
Ekta Bhoraniya's avatar
Ekta Bhoraniya
proBengaluru, India
Product Designer & Framer Expert ✨
3x
Hired
4.9
Rating
40
Followers
FRAMER_EXPERT

expert

Product Designer & Framer Expert ✨
Cover image for AI-Driven Brand Design for AIRA's Sparkling Drink Line
AI-Driven Brand Design for AIRA's Sparkling Drink Line
2
19
Cover image for Lime Breeze Packaging Design for AIRA
Lime Breeze Packaging Design for AIRA
2
22
Cover image for New age social media design
New age social media design
2
3
168
Cover image for AI Platform user onboarding journey
AI Platform user onboarding journey cards
4
164
Google Gemini(2)
Sriharsan BS's avatar
Sriharsan BS
Bengaluru, India
Production ML that turns messy data into decisions!!
New to Contra
Production ML that turns messy data into decisions!!
Cover image for Day one on Contra. New
Day one on Contra. New to the platform, not to the work. I build production ML systems that turn messy data into decisions, and I build the product around them. Where I am strongest: Production ML: risk scoring, prediction, ranking, computer vision Data engineering: pipelines that turn raw, scattered data into reliable features LLM apps: RAG, structured prompting, and evaluation that keeps them honest And I built the full product around it, too: FastAPI and backend services, React and Next.js frontends, dashboards, mobile, and workflow automation when a project needs it. I like owning the whole path from data to a working thing people use. Most builders stop at the demo. I take it to be deployed, monitored, and actually earning its keep. I also provide automation services for your Saas that you don't wanna waste your hours on. Starting today, I am open to work. Independent contracts, remote, a full build, or one piece of one. Startups and small teams turning an idea into a real product are exactly who I want to work with. My projects are on my profile if you want to see how I think before we talk. A piece of my playground work done within hours is posted too. #MachineLearning #AIEngineer #LLM #FastAPI #React #Nextjs #Contra
1
47
Cover image for TechnovaHub chatbot is a branded
TechnovaHub chatbot is a branded AI-powered customer assistant built with React and Node.js, featuring real-time streaming responses via Google Gemini API. It supports bilingual interaction (English and Tamil), voice input, lead capture, and smart intent detection for courses and pricing. The chatbot includes a secure backend proxy, XSS sanitization, and a modular architecture with 57 automated tests ensuring production-grade reliability.
1
71
Cover image for NeuroVision AI is a clinical-grade
NeuroVision AI is a clinical-grade platform that detects neurological gait disorders from walking videos using computer vision and AI. It extracts key biomechanical parameters - cadence, symmetry, tremor index, and freezing episodes, via MediaPipe pose estimation and Isolation Forest anomaly detection, trained on established datasets including DaphNet and PhysioNet GaitPDB. The system delivers AI-powered differential diagnoses, real-time clinical Q&A, and spoken voice reports, all within 60 seconds of video input.
0
50
Cover image for VitalFlow HeartGuard is a cardiovascular
VitalFlow HeartGuard is a cardiovascular risk analytics platform built on the Framingham Heart Study dataset (4,238 patients). It trains three Apache Spark ML models — Logistic Regression, Random Forest, and Gradient Boosted Trees — to predict 10-year coronary heart disease risk. The platform features a FastAPI backend with JWT auth, live what-if analysis, counterfactual interventions, model drift detection, and PDF report generation. A React dashboard visualizes EDA, ROC curves, feature importance, and patient risk trends.
0
58
Google Gemini(2)
Mirwaise Khan's avatar
Mirwaise Khan
Bengaluru, India
AI Product Engineer building custom LLM & RAG pipelines.
New to Contra
AI Product Engineer building custom LLM & RAG pipelines.
Cover image for Hi, I'm Mirwaise Khan, an
Hi, I'm Mirwaise Khan, an AI engineer building practical tools with large language models. This is a project I built — an Enterprise AI Document Assistant. You upload any PDF, and it indexes the content using ChromaDB and vector embeddings. Then you can ask it questions in plain English, and it retrieves the exact relevant section and gives you a grounded answer — with the source cited, so you always know where the information came from. I build systems like this — RAG pipelines, AI chatbots, and document automation tools — using Python, LLMs, and vector databases. If you need something like this for your business, let's talk.
0
42
Cover image for AI Invoice Data Extraction Dashboard Built
AI Invoice Data Extraction Dashboard Built an AI-powered tool that automatically extracts key data from PDF invoices — vendor name, invoice number, date, tax, and total amount — and displays it in an editable dashboard for quick review before saving. Users can upload any invoice PDF, get instant structured extraction, correct fields if needed, and export clean data straight to CSV. Tech used: Python, GenAI/LLM extraction, Pandas, PDF parsing, Streamlit/web UI
0
37
Cover image for Project Title:- Custom Enterprise RAG
Project Title:- Custom Enterprise RAG Assistant | Chat with Documents & PDFs Project Overview: DocuMind is an enterprise-grade Retrieval-Augmented Generation (RAG) assistant designed to eliminate manual data extraction and prevent AI hallucinations. It converts static corporate files (PDFs, reports, resumes, contracts) into an interactive, grounded knowledge engine that provides answers backed by direct page-level citations. The Problem Solved: * Manual Data Hunting: Eliminates hours spent reading through dense, complex documentation. AI Hallucinations: Constrains LLM outputs strictly to uploaded context, ensuring reliable, factual data. Lack of Auditability: Provides exact file names and page references for compliance and verification. Key Technical Features: * Dynamic Indexing: Fast chunking and local vector embedding using Hugging Face models (all-MiniLM-L6-v2). High-Accuracy Vector Search: ChromaDB integration for persistent vector storage and low-latency similarity retrieval. Decoupled Architecture: Asynchronous FastAPI backend paired with a clean, responsive Streamlit chat frontend. Contextual Synthesis: Powered by Google Gemini (gemini-2.5-flash) for cost-effective inference. Tech Stack: Python, FastAPI, LangChain, ChromaDB, Hugging Face, Google Gemini, Streamlit.
0
32
Cover image for 🚀 How I Solved the
🚀 How I Solved the HR Resume Bottleneck with AI Recruiters waste endless hours manually scanning hundreds of unformatted resumes, often missing strong candidates due to fatigue. To solve this, I built an AI Resume Screening Agent that automates the heavy lifting: Semantic Matching: Uses sentence embeddings to understand the true context of skills rather than relying on rigid keyword matching. Instant Leaderboard: Automatically parses multiple PDF resumes, computes match scores, and ranks candidates from best to worst. Actionable AI Feedback: Instantly generates structured breakdowns of candidate strengths, missing skill gaps, and hiring recommendations using the Groq API. Workflow Efficiency: Condenses hours of sorting into seconds with a one-click CSV report export. 🛠 Tech Stack: Python, FastAPI, Groq, SentenceTransformers, Pandas, HTML/CSS. Check the comments for the live public link to test it yourself! What tools do you use to speed up hiring?
0
64
Google Gemini(2)