# RAG-Based Deployments

URL: https://qualixsolutions.com/services/rag-based-deployments/

Retrieval-Augmented Generation systems that ground LLM outputs in your documents, databases, and knowledge bases with citations, accuracy, and guardrails built in.

AI that answers from your data not from its imagination.

#### Why this matters

- 30–60%: of enterprise GenAI use cases now rely on RAG, wherever accuracy, transparency, and source attribution are required. Gartner has identified RAG as a core capability for enterprise generative AI.
- 3–15%: hallucination rate for LLMs depending on domain, according to the Stanford AI Index. Without grounding in verified data, LLMs fabricate confidently - making ungrounded AI unusable for anything business-critical.
- 30%: of GenAI projects abandoned after proof of concept by end of 2025 - frequently killed by hallucination, data quality issues, and grounding failures. The model isn't the hard part. The data pipeline is.

#### What you get

- Use Case & Data Audit
- Data Ingestion & Chunking Pipeline
- Vector Store & Retrieval Architecture
- LLM Integration & Prompt Engineering
- Evaluation & Accuracy Testing
- Monitoring & Continuous Improvement

Service brief: 6-14 weeks. Good for Founders, CTOs, Product Leaders building AI features grounded in proprietary data

#### How we deliver this

1. Discovery workshop: We sit with you, map your workflows, users, and constraints. You leave with a scoped brief — not a proposal full of assumptions.
2. Architecture & Design: System design and UX decisions made before a line of production code.
3. Build: Sprint-based delivery with demos every cycle and one accountable lead.
4. Ship: Deployment to your infrastructure with docs and a clean handover.
5. Stabilize: 30 days of post-launch stabilization while real usage settles in.

#### FAQs

Q: What's the difference between RAG and fine-tuning? Which one do we need?

RAG retrieves information from your data at query time and feeds it to the LLM as context. Fine-tuning changes the model itself by training it on your data. RAG is better for factual accuracy, source citation, and frequently changing data. Fine-tuning is better for teaching the model a specific tone, format, or domain vocabulary. Most enterprise use cases need RAG — or RAG combined with light fine-tuning. We help you decide during the assessment.

Q: What types of data can a RAG system work with?

PDFs, Word documents, knowledge base articles, Confluence/Notion pages, support tickets, internal wikis, structured databases, API responses, Slack transcripts, and more. If the data exists in a format that can be read and chunked, it can be indexed. The real question is whether the data is clean, current, and complete enough to give accurate answers — and that's what we assess first.

Q: How do you prevent hallucinations in RAG systems?

Through architecture, not hope. We use strict prompt engineering that constrains the LLM to answer only from retrieved context. We implement citation requirements so every claim points to a source chunk. We build evaluation suites that measure hallucination rates across hundreds of test queries. And we add monitoring in production to flag responses where confidence or grounding scores drop below threshold. Hallucination can't be eliminated entirely — but it can be measured, contained, and caught.

Q: How much does a RAG deployment cost?

Most RAG deployments with Qualix Solutions fall between $40,000 and $160,000 depending on data volume, source complexity, accuracy requirements, and whether you need a user-facing product or an internal tool. A focused RAG system over a single document corpus sits on the lower end. A multi-source, multi-modal RAG system with hybrid retrieval and production monitoring sits higher. We scope the exact number during a strategy session.