Build an AI/RAG Proof of ConceptShiju Viswanthampi
PAILABS will design and build a functional Retrieval-Augmented Generation (RAG) Proof of Concept that enables users to ask questions in natural language and receive contextual, source-grounded answers based on your organization's own documents and knowledge.
We can work with PDFs, policies, manuals, knowledge bases, product documentation and other enterprise content to demonstrate how Generative AI can securely retrieve relevant information and generate useful responses.
A typical POC covers document ingestion → text processing → embeddings → vector search → retrieval → LLM response generation → source references → user interface.
Depending on the requirement, the solution can use technologies such as Azure OpenAI/OpenAI, Azure AI Search, Python, FastAPI, React, PostgreSQL/pgvector and other vector databases.
The objective is to give you a working prototype, rather than just an AI strategy document, so your organization can validate the use case before investing in a full production implementation.
Typical deliverables: Working RAG prototype, document ingestion pipeline, semantic/vector search, AI Q&A interface, source-grounded responses/citations, basic admin/configuration, architecture overview, demonstration session, and recommendations for production scaling.
Starting at$2,500
Duration2 weeks
Tags
RAG
Azure OpenAI
Generative AI
Knowledge Base
·LLM
OpenAI API
Python
Semantic Search
Vector Database ·
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Shiju Viswanthampi proChennai, India
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Build an AI/RAG Proof of ConceptShiju Viswanthampi
Starting at$2,500
Duration2 weeks
Tags
RAG
Azure OpenAI
Generative AI
Knowledge Base
·LLM
OpenAI API
Python
Semantic Search
Vector Database ·
PAILABS will design and build a functional Retrieval-Augmented Generation (RAG) Proof of Concept that enables users to ask questions in natural language and receive contextual, source-grounded answers based on your organization's own documents and knowledge.
We can work with PDFs, policies, manuals, knowledge bases, product documentation and other enterprise content to demonstrate how Generative AI can securely retrieve relevant information and generate useful responses.
A typical POC covers document ingestion → text processing → embeddings → vector search → retrieval → LLM response generation → source references → user interface.
Depending on the requirement, the solution can use technologies such as Azure OpenAI/OpenAI, Azure AI Search, Python, FastAPI, React, PostgreSQL/pgvector and other vector databases.
The objective is to give you a working prototype, rather than just an AI strategy document, so your organization can validate the use case before investing in a full production implementation.
Typical deliverables: Working RAG prototype, document ingestion pipeline, semantic/vector search, AI Q&A interface, source-grounded responses/citations, basic admin/configuration, architecture overview, demonstration session, and recommendations for production scaling.
$2,500