You will get Build a Lovable-powered SaaS with Supabase backend
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Project details
I will design, develop, and deploy a fully functional web application or SaaS product using Supabase as the backend and Lovable AI for intelligent features. The project includes user authentication, database setup, responsive front-end design, API integration, and seamless deployment. Depending on the chosen tier, I will implement a single Lovable AI-powered feature for automation, insights, or recommendations, with optional dashboards and monitoring. This ensures your product is production-ready, scalable, and delivers real business impact.
What's included
| Service Tiers |
Starter
$1,450
|
Standard
$2,500
|
Advanced
$4,500
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 25 days |
Number of Revisions | 2 | 3 | 5 |
Design Customization | |||
Content Upload | |||
Responsive Design | |||
Source Code |
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About Aru
Lead Data Scientist - AI/ML| Fractional CTO | AI Developer
100%
Job Success
Paris, France - 9:52 pm local time
With 7+ years in ML/AI, I specialize in LLM systems, RAG architectures, and MLOps, helping teams move from prototype to reliable, scalable AI in production.
My focus is not just building models but ensuring they perform under real-world constraints: latency, cost, hallucination control, and continuous improvement.
What I build
-End-to-end ML pipelines: ingestion, training, deployment, monitoring
-RAG systems with proper evaluation harnesses (retrieval quality, regression catching)
-LLM observability: prompt versioning, tracing, feedback loops
-Scalable inference (real-time and batch) with GPU optimization
-CI/CD for ML with MLflow, Airflow, automated retraining
-AI agents with guardrails and reliable tool use
Selected results
-Fraud detection pipeline (real-time inference) → ~$890K in losses prevented
-Forecasting system using ensemble models → 31% accuracy lift over baseline
-Multiple LLM products running in production under client SLAs
-$3M+ in measurable business impact across deployments (happy to walk through specifics on a call)
Stack
-Languages/frameworks: Python, FastAPI
-LLMs: OpenAI, Anthropic, HuggingFace, LangChain, LlamaIndex
-Vector DBs: Pinecone, Weaviate, ChromaDB, FAISS
-Infra: AWS, Docker, Supabase, MLflow, Airflow
How I work
-Replies in 0–4 hours on business days
-Production-grade code with tests and docs, not throwaway notebooks
-Clear scope, direct communication, focused on business outcomes
-Revenue, automation, speed-to-market — that's the scoreboard
Let's turn your AI idea into a working product. Send an invite with your project details and I'll get back to you with a clear plan.
Steps for completing your project
After purchasing the project, send requirements so Aru can start the project.
Delivery time starts when Aru receives requirements from you.
Aru works on your project following the steps below.
Revisions may occur after the delivery date.
Discuss requirement (optional meeting)
Prototype