SolutionUpdated March 2026

RAG pipeline development that goes beyond the demo

Most RAG demos hit 60-70% accuracy and stall. We build production-grade RAG systems that achieve 95%+ accuracy with citation-backed responses, optimized retrieval, and continuous improvement. The same technology that powers Veritas.

See Veritas

Your RAG prototype works in demos but fails in production

Building a RAG demo takes a weekend. Building a production RAG system that is accurate, fast, and reliable takes significantly more. Most teams hit a wall at 60-70% accuracy: the system retrieves the wrong chunks, generates hallucinated answers, cannot handle complex queries, and breaks on document formats it has not seen before. The gap between demo and production is where most RAG projects die.

60-70%

Typical accuracy of basic RAG implementations

80%

Of AI POCs never reach production

95%+

Accuracy target for production RAG systems

Weeks

Not months, to build a production RAG system right

How Optivus builds production RAG systems

We build the full RAG pipeline: from document ingestion and intelligent chunking to vector store optimization, hybrid retrieval, re-ranking, and citation-backed response generation. Every component is tuned for your specific data and use case. This is the same technology stack that powers Veritas, our knowledge-grounded content platform.

01

Audit your data

Analyze your document corpus: formats, volumes, structure, update frequency. Define use cases and accuracy requirements.

02

Design retrieval architecture

Choose chunking strategy, embedding model, vector store, and retrieval approach optimized for your specific data characteristics.

03

Build and iterate on accuracy

Build the pipeline, test against ground truth, and iterate. Target: 95%+ accuracy on your real queries with your real data.

04

Deploy with monitoring

Production deployment with query analytics, accuracy tracking, feedback loops, and continuous improvement from user interactions.

Delivered byVeritas

Custom RAG pipeline development using the same technology stack that powers Veritas. Production-ready in 2-4 weeks.

Key capabilities

Multi-format document ingestion

PDF, Word, emails, spreadsheets, databases, APIs, web pages. Handle any source your organization uses.

Intelligent chunking

Context-aware chunking strategies that preserve meaning across chunk boundaries. Not naive 500-token splits.

Hybrid search

Combine semantic search (vector similarity) with keyword search (BM25) for better retrieval across query types.

Re-ranking for relevance

Cross-encoder re-ranking that evaluates retrieved chunks against the actual query for higher precision.

Citation-backed responses

Every response traces back to specific source documents. Users can verify any claim. No unsourced assertions.

Hallucination detection

Confidence scoring and factual grounding checks. Responses that cannot be supported by retrieved documents are flagged.

Results you can expect

95%+

Accuracy target for production RAG deployments

Cited

Every response traceable to source documents

2-4 weeks

From data audit to production deployment

Continuous

Improvement from user feedback and query analytics

Built with:LangChainLlamaIndexPinecone / Weaviate / pgvectorClaude / GPT-4Custom re-ranking modelsPythonFastAPI

Our AI implementation process

Every engagement follows the same four-phase structure.

01

Scope

Map the workflow, define success criteria, lock deliverables.

02

Build

Weekly working demos. Direct channel with the build team.

03

Ship

Production deployment on your cloud with monitoring.

04

Scale

Optimize on real usage. Expand to adjacent workflows.

Learn more about The Optivus Method

Frequently asked questions

RAG (Retrieval Augmented Generation) connects large language models to your specific data. Instead of answering from general knowledge (which leads to hallucinations), the LLM retrieves relevant information from your documents and generates responses grounded in that data. It is how you make LLMs useful for your business without fine-tuning.
Basic RAG implementations typically achieve 60-70% accuracy. Production-grade RAG systems with proper chunking, hybrid retrieval, re-ranking, and citation backing can achieve 95%+ accuracy. The difference is engineering effort and domain-specific optimization.
RAG for knowledge retrieval (answering questions from your documents). Fine-tuning for behavior adaptation (making the model write in your style or follow specific formats). Most enterprise use cases need RAG, not fine-tuning. Some need both. We help you decide based on your specific requirements.
A production RAG system for a focused use case takes 2-4 weeks. Larger deployments with multiple data sources, complex retrieval requirements, and extensive accuracy testing take 4-8 weeks.
Any structured or unstructured data: PDFs, Word documents, emails, Confluence, SharePoint, Google Drive, Slack, databases, APIs, web pages. If your organization stores information somewhere, we can connect a RAG pipeline to it.
Multiple layers: citation backing (every claim traced to a source), confidence scoring (low-confidence responses flagged), factual grounding checks (assertions compared against retrieved context), and retrieval quality monitoring (tracking whether the right documents are being retrieved).

Related solutions

Enterprise AI Knowledge BaseAI Agents for EnterpriseAI Content Generation

For your industry

AI for Professional ServicesAI for Financial Services

From our blog

RAG vs Fine-Tuning for Enterprise AI

Read article

LLM Application Development Guide

Read article

Ready to get started?

Book a 25-minute call. Bring your workflow and we will show you exactly how we would approach it.

See What We Have Built