Build an End-to-End Applied AI Prototype / PoC by Timal PathiranaBuild an End-to-End Applied AI Prototype / PoC by Timal Pathirana
Build an End-to-End Applied AI Prototype / PoCTimal Pathirana
Engineering quality: This is not intended to be a throwaway AI demo. The prototype will be built with sound engineering practices including modular architecture, separation of concerns, typed interfaces, environment-based configuration, validation, testing and maintainable code.
Best for: AI product concepts, internal AI tools, RAG systems, document intelligence, structured data extraction, AI-enabled APIs, workflow automation and early-stage AI SaaS ideas.
Turn your AI idea into a working technical prototype built with real software engineering practices.
I’ll take a clearly defined AI use case from concept to an end-to-end working proof of concept, including architecture, model integration, backend logic, data flow, testing and technical handover.
This service is designed for startups and software teams that want to validate whether an AI product, feature or workflow is technically viable before investing in a full production build.
I focus on Applied AI- practical systems built around LLMs, retrieval, structured outputs, tool use, automation and business data.
What’s included
• Technical discovery and solution architecture
• AI model/provider selection
• End-to-end prototype implementation
• LLM/API integration
• Prompt and system instruction design
• Structured outputs and schema validation
• RAG, embeddings and vector retrieval where appropriate
• Tool/API integration where required
• Backend services and business logic
• Error handling and resilience
• Environment-based configuration and secure secret handling
• Logging and observability basics
• Automated testing for critical application logic
• Clean, modular and maintainable project structure
• Security and data-handling considerations
• Technical documentation and handover
• Recommendations for taking the prototype into production
What’s not included
• Full production SaaS platforms
• Large or complex frontend applications
• Enterprise-scale infrastructure or DevOps implementation
• Complex multi-agent ecosystems
• Large-scale data migration or data cleansing
• Production deployment at significant scale
• Third-party API, model, hosting or cloud usage fees
• Major changes to unrelated parts of an existing application
If your project requires any of the above, I can review the requirements and provide a separate scope.
Build an End-to-End Applied AI Prototype / PoCTimal Pathirana
Starting at$1,500
Duration2 weeks
Tags
Anthropic
.NET
OpenAI
Python
RAG
AI Developer
LLM
Software Architect
Artificial Intelligence
Engineering quality: This is not intended to be a throwaway AI demo. The prototype will be built with sound engineering practices including modular architecture, separation of concerns, typed interfaces, environment-based configuration, validation, testing and maintainable code.
Best for: AI product concepts, internal AI tools, RAG systems, document intelligence, structured data extraction, AI-enabled APIs, workflow automation and early-stage AI SaaS ideas.
Turn your AI idea into a working technical prototype built with real software engineering practices.
I’ll take a clearly defined AI use case from concept to an end-to-end working proof of concept, including architecture, model integration, backend logic, data flow, testing and technical handover.
This service is designed for startups and software teams that want to validate whether an AI product, feature or workflow is technically viable before investing in a full production build.
I focus on Applied AI- practical systems built around LLMs, retrieval, structured outputs, tool use, automation and business data.
What’s included
• Technical discovery and solution architecture
• AI model/provider selection
• End-to-end prototype implementation
• LLM/API integration
• Prompt and system instruction design
• Structured outputs and schema validation
• RAG, embeddings and vector retrieval where appropriate
• Tool/API integration where required
• Backend services and business logic
• Error handling and resilience
• Environment-based configuration and secure secret handling
• Logging and observability basics
• Automated testing for critical application logic
• Clean, modular and maintainable project structure
• Security and data-handling considerations
• Technical documentation and handover
• Recommendations for taking the prototype into production
What’s not included
• Full production SaaS platforms
• Large or complex frontend applications
• Enterprise-scale infrastructure or DevOps implementation
• Complex multi-agent ecosystems
• Large-scale data migration or data cleansing
• Production deployment at significant scale
• Third-party API, model, hosting or cloud usage fees
• Major changes to unrelated parts of an existing application
If your project requires any of the above, I can review the requirements and provide a separate scope.