The optimal LangChain stack combines Python and FastAPI for the application layer, OpenAI or Anthropic API for LLM access, Supabase with pgvector or Pinecone for vector storage, and LangSmith for observability and prompt management. Pydantic manages structured output validation from LLM responses. For deployment, Docker containers on AWS ECS Fargate or Google Cloud Run offer scalable infrastructure for LangChain-based AI API services.
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Oleh focuses on EVM smart contract engineering and blockchain data pipelines, with ~3.5 years of commercial experience as a Blockchain Software Develo. He delivers production Solidity, TypeScript and Rust code for token flows... Read More
Nine years developing data science solutions across agritech, defence, fintech, and sports analytics. Built Quantum's entire DS department from scratch — hired over 30 people, created learning pathways, and mentored MSc and P... Read More
Pavlo mostly specialises in Backend development with a strong emphasis on quantitative analysis and data processing. With nine years of experience, he is proficient in Python, Node.js and JavaScript, utilising frameworks such... Read More
Victoriia is a skilled Flutter Developer with 4 years of experience in mobile application development. She specializes in frameworks such as Flutter, leveraging JavaScript, DART, and utilizes databases like MySQL and Firebase... Read More
Cortance delivered the project within schedule and according to the end client's requirements. The team had a clear workflow and was responsible, professional, and kind. They translated the end client's vision into the product and faced any challenges with patience and impressive responsiveness.
Cortance delivered a functional, stable system on time, receiving positive feedback from the end client. The team was responsive to feedback and quickly resolved issues, communicating via virtual meetings, emails, and messaging apps. Their proactive approach impressed the client.
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