RAG Systems That Answer
From Your Data, Not Thin Air
A vanilla LLM makes things up. A well-built RAG system cites sources, stays grounded, and gets smarter as your knowledge base grows. We build production RAG pipelines with the retrieval precision, evaluation rigour, and hallucination controls that enterprise applications demand.
Our Service Capabilities
End-to-end capability — from strategy and build to integration, monitoring, and ongoing support.
Document Ingestion & Chunking
Intelligent document parsing, chunking, and preprocessing — handling PDFs, Word, HTML, and code with layout-aware chunking strategies that preserve semantic coherence.
Embedding & Indexing
Embedding model selection and evaluation (OpenAI, Cohere, BGE, E5) — with vector index optimisation for your corpus size, query volume, and latency requirements.
Hybrid Search Architecture
Combining dense vector search with BM25 sparse retrieval and cross-encoder re-ranking — delivering higher recall and precision than vector search alone.
Citation & Attribution Enforcement
Grounding mechanisms that require the LLM to cite specific sources, with automatic source attribution and confidence scoring — so every answer is auditable.
RAG Evaluation Framework
Automated RAGAS evaluation pipelines with golden test sets — measuring context recall, answer faithfulness, answer relevance, and regression testing on every deployment.
Agentic RAG & Multi-hop Reasoning
Advanced RAG architectures for complex queries requiring multi-hop reasoning — iterative retrieval, query decomposition, and result aggregation across multiple knowledge sources.
Our Engagement Process
A disciplined, outcome-focused approach from first call to go-live.
- 1
Knowledge Audit & Source Mapping
Inventory your knowledge sources — documents, databases, APIs — and design the ingestion and update strategy.
- 2
Chunking & Embedding Strategy
Design and test chunking approaches, select and evaluate embedding models on your specific corpus and query patterns.
- 3
Retrieval Pipeline Build
Implement vector index, hybrid search, re-ranking, and context assembly — with iterative retrieval precision benchmarking.
- 4
LLM Integration & Grounding
Connect the retrieval pipeline to your LLM with grounding instructions, citation enforcement, and confidence scoring.
- 5
Evaluation & Production Launch
Build the RAGAS evaluation pipeline, run golden-set testing, deploy to production, and set up ongoing evaluation monitoring.
Tools & Frameworks We Master
A production-tested, vendor-agnostic stack built for enterprise security and compliance requirements.
Orchestration
Vector Databases
Embedding Models
Evaluation
Use Cases by Industry
Production AI systems we have built across regulated, data-heavy industries.
Internal Knowledge Base Q&A
RAG over 80,000 internal documents — employees get cited answers in under 3 seconds. Reduced internal support tickets 60%, 95%+ answer faithfulness on eval set.
Contract Intelligence RAG
RAG system over 200,000 legal contracts — clause extraction, obligation summarisation, and risk flagging with source citations. Cuts review time 80%.
Clinical Knowledge RAG
HIPAA-compliant RAG over clinical guidelines and drug databases — physicians query in plain English, responses include chapter-level citations, 96% faithfulness score.
Regulatory RAG Assistant
RAG over 15,000 regulatory documents — compliance officers query in plain English, get answers with precise regulation citations, audit-logged.
Product Documentation RAG
RAG chatbot grounded in 5,000 product documentation pages — resolves 72% of support queries without human agent, 94% user satisfaction.
Scientific Literature RAG
Multi-source RAG over PubMed and internal research documents — research team queries across 500,000+ papers, with structured citation output.
What Teams Say After Shipping with Us
Real results from teams who needed RAG systems to work in production, not just in a demo.
AndolaSoft has been a valued partner providing excellent customer service. Issues with clients or troubleshooting are handled in a timely manner and positive resolution is always the outcome.
I got a recommendation on AndolaSoft. They are more than half the cost, they have a can-do attitude, and they are responsive, timely, and easy to work with.
Andolasoft team is very hardworking, dedicated and professional that follows through with their goals. The technical leadership is also a superior value to any other developers.
Frequently Asked Questions
RAG (Retrieval-Augmented Generation) retrieves relevant documents at query time and uses them to ground LLM responses. Unlike fine-tuning, RAG works with knowledge that changes frequently, provides citations, and does not require expensive retraining when your knowledge base updates.
Ready to build your RAG system?
Tell us about your knowledge base. We will design a production-grade RAG pipeline with the retrieval precision, citation enforcement, and evaluation rigour your application demands.