AI Answers From Your Knowledge, With Sources.
Enterprise RAG development services that ground every answer in your documents, respect existing access rights, and prove accuracy with evals, running privately in your own cloud.
- 95%+source-grounded answers
- 8-12weeks, pilot to production
- 4-6weeks per added domain
- 24/7quality monitoring
Why Most RAG Projects Disappoint.
The model is rarely the problem. Retrieval, access control, and measurement are.
- 01Wrong Passages RetrievedNaive chunking and vector-only search miss the answer the user needed.
- 02Data Leaks Across TeamsAssistants show content users aren't allowed to see.
- 03Nobody Measures AccuracyWithout evals, hallucinations surface in front of customers.
Definition
What Is Retrieval-Augmented Generation (RAG)?
Retrieval-augmented generation (RAG) is an AI architecture that searches your own documents for the passages relevant to a question, then has a large language model answer using only those passages, with citations. SMI TechSolutions builds production RAG with hybrid retrieval, access control, and automated accuracy evals.
Use cases
Where We Apply RAG
Customer Support
FromAgents searching wikis mid-callToAn agent assistant with cited answersGrounded in product docs and ticket history.
Legal & Compliance
FromManual contract and policy reviewToContract Q&A and policy lookupsRetrieval-augmented generation for legal and compliance documents.
Engineering
FromTribal knowledge in old reposToCode-aware assistant over private reposAnswers from code, ADRs, and design docs.
Healthcare
FromGuideline binders and intranet PDFsToGuideline lookups with citations and confidenceClinical teams see the source behind every answer.
Enterprise Knowledge
FromScattered SharePoint and ConfluenceToOne knowledge assistant for the companyEnterprise knowledge management using RAG and LLMs.
RAG vs fine-tuning
RAG vs Fine-Tuning: Which Do You Need?
Use RAG when answers must come from your current documents, change often, need citations, or must respect access rights. Use fine-tuning when the model needs a new style, format, or specialized behavior. Many enterprise systems combine RAG with light prompt engineering and fine-tune only when evals show a gap.
| Approach | What it changes | Best for | Limits |
|---|---|---|---|
| Prompt engineering | Instructions only | Simple tasks on general knowledge | No access to your private data |
| RAGYour data, with citations | What the model reads at answer time | Answers from changing internal knowledge, with sources | Quality depends on retrieval design |
| Fine-tuning | The model's weights | Consistent style, format, or domain behavior | Costly to update; no citations; knowledge goes stale |
How we deliver
From Architecture Review to Production RAG
- 1Week 1
Review
60-minute architecture review; success criteria and golden Q&A set agreed.
- 2Within 8-12 weeks
Build pilot
Connectors, chunking, hybrid index, and retrieval tuned on one domain.
- 3Within 8-12 weeks
Eval & harden
Retrieval and answer metrics tracked; regression gates block bad releases.
- 44-6 weeks each
Expand
New domains added on the same platform.
- Security built in
- Existing user entitlements respected at retrieval time
- Deployed in your tenant
- Private, zero-retention model endpoints
- Self-hosted open-source models for sensitive data
FAQ
Frequently Asked Questions
What is retrieval-augmented generation?
Retrieval-augmented generation (RAG) is an AI approach that retrieves relevant passages from your own documents and gives them to a large language model, which answers from those passages and cites them. It keeps answers current and traceable without retraining the model.
Should we use RAG or fine-tuning?
Use RAG when answers must come from your own, frequently changing knowledge with citations and access control. Use fine-tuning to change a model's style or specialized behavior. SMI TechSolutions usually starts with RAG and adds fine-tuning only when evals show it would help.
Can RAG eliminate hallucinations?
No production AI system is completely hallucination-free. SMI TechSolutions reduces hallucinations with hybrid retrieval, re-ranking, and answers restricted to retrieved sources, then measures groundedness continuously so issues are caught before users see them.
How do you keep RAG secure?
SMI TechSolutions enforces your existing permissions at retrieval time, so users only see answers drawn from documents they can already open. Systems run in your cloud tenant, using private model endpoints with zero data retention or self-hosted models for sensitive content.
Where does our data stay?
Your data stays in your environment. SMI TechSolutions deploys RAG inside your cloud tenant, connects to frontier models through private endpoints configured for zero retention, and can run open-source models fully in-house when data residency rules require it.
How do you measure RAG accuracy?
SMI TechSolutions builds a golden Q&A set with your experts, then tracks retrieval metrics such as Recall@k and MRR and answer metrics such as groundedness, using LLM-as-judge scoring calibrated to human labels. Regression gates block releases that lower quality.
What is a RAG architecture?
A RAG architecture is the pipeline that turns a question into a grounded answer: ingestion, chunking, indexing, retrieval, generation, and evaluation. SMI TechSolutions uses hybrid dense and keyword indexes, a cross-encoder re-ranker, and Claude as the primary model.
What is RAG as a service?
RAG as a service means a provider builds, hosts, and maintains the retrieval pipeline, indexes, and evals for you. SMI TechSolutions can run your RAG platform under a managed engagement, or build it and hand it over to your team.
How long does it take to build a RAG system?
SMI TechSolutions typically takes a single-domain RAG system from architecture review to production in 8 to 12 weeks. Each additional knowledge domain then takes about 4 to 6 weeks on the same platform.
Which data sources can RAG connect to?
SMI TechSolutions connects RAG to SharePoint, Confluence, Google Drive, Slack, Jira, GitHub, file shares, and custom databases. Each connector carries the source system's permissions, so access rules travel with the content.
What is knowledge management AI?
Knowledge management AI uses search, retrieval, and language models to help people find and use what an organization already knows. SMI TechSolutions delivers it through RAG assistants that answer in plain language and cite the source documents.
Ready to Build Production RAG?
Book a 60-minute architecture review. Agree success criteria and leave with an 8-12 week path to production.
60-minute RAG architecture review
Success criteria agreed and an 8-12 week path to production.
Book an Architecture Review- Nearly two decades of delivery
