RAG Application Development
RAG retrieves the most relevant passages from your content and gives them to a language model, so answers reflect your documents instead of the model's general knowledge. It powers knowledge assistants, support bots, document Q&A and agents that need facts.
We build the full pipeline: ingestion and parsing, chunking, embeddings, vector and hybrid search, re-ranking, prompt design and evaluation, plus GraphRAG and agentic RAG for multi-step questions. Our enterprise search background is what makes the retrieval step work.
Why ProsperaSoft for RAG Development
Most RAG systems fail on retrieval, not on the language model: poor chunking, missing metadata, weak ranking and no filters. We fix those with search engineering from years of Solr, Elasticsearch and OpenSearch projects.
We measure before and after every change with a test set of real questions, track retrieval and answer accuracy, and deploy on your cloud with the model of your choice. See our legal RAG chatbot and healthcare RAG chatbot case studies.
RAG Development Services
RAG Assistants and Chatbots
Knowledge assistants and support bots that answer from your content with source citations.
Document Ingestion Pipelines
Parsing PDFs, Office files, web pages, tables and scans with metadata, deduplication and refresh.
Hybrid Retrieval and Re-ranking
Keyword and vector search combined with re-ranking models, filters and permission checks.
GraphRAG and Agentic RAG
Knowledge graphs and multi-step retrieval for questions that span many documents.
RAG Evaluation
Test sets and metrics for retrieval recall, answer correctness and faithfulness, run on every change.
RAG Fixes and Optimisation
Diagnose wrong or missing answers in existing RAG systems and improve accuracy, latency and cost.
RAG Use Cases
Internal Knowledge Assistants
Policies, procedures and technical documentation answered instantly.
Customer Support
Answers from manuals, FAQs and past tickets.
Legal and Compliance
Contracts and regulations searched and summarised with sources.
Product and Sales
Specifications, pricing and proposals found in seconds.
TECHNICAL EXPERTISE
Frequently asked questions
What is retrieval-augmented generation?
RAG retrieves the most relevant passages from your documents or databases and gives them to a language model, so answers are grounded in your data instead of the model's general knowledge.
Why do RAG systems give wrong answers, and how do you fix it?
Common causes are poor chunking, weak retrieval, missing metadata filters and multi-hop questions. We fix them with better chunking, hybrid search, re-ranking, query rewriting and evaluation on real questions.
Which vector databases do you use?
Pinecone, Weaviate, Qdrant, pgvector, Elasticsearch and OpenSearch vector search, chosen based on scale, hosting and existing infrastructure.
How do you measure RAG quality?
We build a test set of real questions with expected answers and track retrieval accuracy, answer correctness and citation quality every time the pipeline changes.
Can RAG respect document permissions?
Yes. We store access control information with each document and filter retrieval per user, so answers only use content the user is allowed to see.