Your AI is answering.
Is it answering correctly?
Retrieval-Augmented Generation grounds your AI in your verified internal knowledge -- eliminating hallucinations, enforcing access controls, and delivering answers your organization can trust and act on.
Your AI is confident. That is the problem.
An LLM without grounding invents answers with the same tone and confidence as accurate ones. In regulated industries, that is not a product deficiency -- it is a liability.
Your knowledge exists. It is just unreachable.
The average enterprise has its critical knowledge spread across SharePoint, Teams, ERP systems, and employee inboxes. AI cannot answer from what it cannot access and verify.
Proof of concept is not production.
Demo environments rarely account for access controls, data freshness, or multi-source retrieval. Most enterprise RAG projects stall between pilot and production for exactly these reasons.
Retrieval-Augmented Generation -- enterprise grade
RAG is an AI architecture that separates knowledge from reasoning. Instead of relying on what a model was trained on, a RAG system retrieves relevant content from your approved knowledge sources at the moment a question is asked -- then uses the LLM only to synthesize and present that retrieved content.
The result is an AI that answers from your documentation, your policies, and your data -- not from a general training set that may be months or years out of date.
User asks a question
Natural language query from your employee or customer interface
Query is embedded and searched
Semantic vector search finds the most relevant content in your knowledge base
Access controls are enforced
Only content the user is permitted to see is retrieved and passed forward
LLM synthesizes the answer
The model generates a grounded response from retrieved content only
Source citations returned
Every answer links back to the document it came from -- auditable and verifiable
Built for the knowledge problems that matter most
Policy & Compliance Search
Employees query internal policy and receive a grounded, cited answer -- not a keyword match or a general AI response. Built for banking, energy, and industrial organizations with complex regulatory environments.
Field Operations Knowledge
Field engineers and operations staff access technical manuals, procedures, and maintenance records through a natural language interface -- without needing to know where documentation is stored or how to search it.
Contract & Document Intelligence
Legal and commercial teams query across contract repositories, extract key terms, and compare documents against standard templates. RAG eliminates the need to manually read every document for every review cycle.
Employee Onboarding & HR
New hires get accurate, current answers to onboarding questions from your HR knowledge base -- not outdated intranet articles or answers from whoever happens to be available. Onboarding time drops significantly.
Customer & Partner Portals
External-facing RAG agents answer customer and partner questions using only your approved product and support documentation -- with citations and escalation paths when the answer is not in the knowledge base.
Financial & Audit Intelligence
Finance teams query across financial reports, audit documentation, and regulatory filings. RAG grounds every answer in source documents, creating a defensible audit trail for every AI-assisted finding.
From scattered knowledge to governed retrieval
ClarityArc delivers enterprise RAG in four phases. Each phase has defined deliverables, clear outcomes, and no ambiguity on scope or investment before you commit to the next.
Clarify
We map your knowledge sources, identify governance gaps, define access control requirements, and scope the retrieval architecture before writing a line of code.
Architect
We design the full RAG pipeline -- embedding model, vector store, retrieval strategy, chunking approach, and integration points with your existing Microsoft or Azure environment.
Build
We construct, test, and validate the retrieval system against your actual content. Access controls are tested against your security model before anything goes to production.
Activate
We move to production, train your users, monitor retrieval accuracy against real queries, and tune the system until it performs to the standard your organization requires.
Most RAG implementations work in demos.
Few hold up in production.
| Practice Area | Standard Approach | ClarityArc Approach |
|---|---|---|
| Data governance | Index all available content and filter after retrieval | Govern at ingestion -- only approved, classified content enters the knowledge base |
| Access controls | Single-user retrieval layer; no per-user permission enforcement | Per-user access controls enforced at retrieval time -- answers respect your security model |
| Chunking strategy | Fixed-size chunking applied uniformly across all document types | Content-aware chunking tuned per document type -- policies, procedures, and manuals each handled differently |
| Freshness | Manual re-indexing when someone remembers to trigger it | Automated incremental indexing -- knowledge base stays current as source documents change |
| Evaluation | Qualitative review by developers during build | Structured evaluation framework with recall, precision, and faithfulness metrics tracked in production |
| Hallucination risk | Prompt engineering to reduce but not eliminate model invention | Strict grounding constraints -- model is instructed to decline rather than invent when content is absent |
What enterprise teams ask before starting a RAG project
How is RAG different from just giving ChatGPT access to our documents?
A consumer AI tool with document upload has no access controls, no governance over what content is used, no audit trail, and no mechanism to keep knowledge current as your documentation changes. Enterprise RAG is a purpose-built retrieval architecture that enforces your security model, tracks what sources every answer came from, and operates entirely within your infrastructure and data classification policy.
Do we need Azure OpenAI or can we use other LLMs?
ClarityArc deploys primarily on Azure OpenAI because it operates within your existing Microsoft trust boundary -- your data never leaves your Azure tenant. That said, we have deployed RAG pipelines on other LLMs for organizations with existing investments elsewhere. The retrieval architecture is largely model-agnostic; the LLM is the synthesis layer at the end of the pipeline.
How long does a typical enterprise RAG implementation take?
A well-scoped single-domain RAG deployment -- one knowledge domain, one user group, governed sources -- typically takes 8 to 14 weeks from Clarify to production. Multi-domain or multi-tenant deployments, or those requiring significant data remediation, take longer. We scope this explicitly in Phase 01 so there are no surprises mid-project.
How much does enterprise RAG implementation cost?
Implementation cost varies by scope -- number of knowledge sources, access control complexity, integration requirements, and data quality. Typical engagements range from $40,000 for a well-defined single-domain deployment to $150,000+ for multi-domain, multi-tenant enterprise builds. We publish a detailed cost guide to help you build a business case before we talk.
Can RAG work with our existing SharePoint and Microsoft 365 environment?
Yes -- and it is one of ClarityArc's primary deployment patterns. SharePoint is the dominant enterprise knowledge store in the Microsoft ecosystem, and we build retrieval pipelines that index SharePoint content while respecting existing permission structures. Microsoft Copilot and Copilot Studio are common deployment surfaces for the finished agent.
Intelligent Knowledge Systems
View the full practice →Ready to build an enterprise RAG system that actually works in production?
Whether you have a defined project or a knowledge problem you have not fully scoped yet, we start with a focused discovery conversation -- no commitment required beyond that.