RAG Pipeline Development
RAG Pipeline Development — Ground Your AI in Real Data, Not Hallucinations
Hallucinating AI is useless AI. Kovil AI builds RAG systems that retrieve the right information at the right time — reducing hallucination rates and making your LLM trustworthy in production.
What We Build
RAG pipelines over your documents, PDFs, databases, and internal wikis
Optimized chunking strategies — fixed, semantic, or document-structure-aware
Hybrid search combining dense vector and sparse BM25 retrieval
Re-ranking layers that surface the most relevant context before the LLM sees it
RAGAS evaluation pipelines to measure retrieval accuracy and faithfulness in production
Tech Stack
How It Works
Audit Your Data
We assess your document types, volumes, and query patterns to design the right RAG architecture.
Build & Evaluate
Milestone-gated build with RAGAS evaluation at each phase. You see retrieval accuracy before moving forward.
Deploy & Monitor
Production deployment with evaluation dashboards, latency monitoring, and hallucination rate tracking.
Legal / LegalTech
RAG Contract Review Agent — Trained on Firm's Own Precedent Library
94% Clause Automation
78% Faster Review
Frequently Asked Questions
What is RAG?
RAG (Retrieval-Augmented Generation) is a technique that grounds LLM responses in your proprietary data. At query time, the system retrieves the most relevant documents from your knowledge base and passes them to the LLM as context — so the AI answers from your data, not just its training.
When should I use RAG vs fine-tuning?
RAG is right for most enterprise use cases — when your data changes frequently, when you need source attribution, or when you want to avoid retraining costs. Fine-tuning is better for consistent output format or style. Our engineers will assess your use case and recommend the right approach.
How do you handle document chunking?
Chunking strategy depends on your document types. We choose from fixed-size, sentence, recursive, semantic, or document-structure-aware chunking based on how your content is structured and how users query it. Poor chunking is one of the most common causes of RAG failure.
How do you reduce hallucinations in a RAG system?
Multiple layers: high-quality chunking and retrieval, re-ranking to surface the most relevant context, structured prompting that constrains the model to the retrieved context, output validation, and evaluation frameworks (RAGAS) to measure retrieval accuracy and faithfulness.
What vector databases do you work with?
Pinecone, Weaviate, Chroma, pgvector (PostgreSQL), Qdrant, and Milvus. We choose based on your scale, latency requirements, and existing infrastructure. For many production systems, pgvector on an existing Postgres instance is the simplest and most maintainable choice.
Can you improve an existing failing RAG system?
Yes — Kovil AI's App Rescue squad diagnoses failing RAG systems. Common issues: poor chunking, low retrieval relevance, missing re-ranking, context window overflow, and no evaluation pipeline. We audit and fix.
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Fixed price. Milestone-gated. Zero delivery risk. Zero termination fees.
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