Capability 04 — Knowledge (RAG)
AI that reads your documents. Answers with sources.
A model alone only knows what it was trained on — public data frozen in time. RAG connects it to your documents, your policies, your knowledge base. Every answer comes with a source you can verify.
Overview
What is RAG?
Retrieval-Augmented Generation grounds AI responses in your documents. When someone asks a question, the system:
Search
Searches your document library for relevant passages.
Retrieve
Retrieves the most relevant ones.
Context
Passes them to the model as context.
Answer
The model answers based on what it just read — with citations.
The model becomes an expert on your organization, updated instantly as you add documents. No retraining. No fine-tuning. Just connect and ask.
Example
Before and after.
What's our policy on remote work for employees outside Puerto Rico?
“Remote work policies vary by company and jurisdiction. Generally, companies allow remote work with manager approval and may require employees to be in the same country for tax purposes. You should consult your HR department for specific policies.”
“Per the Remote Work Policy (revised March 2026, section 3.1): employees outside Puerto Rico may work remotely if their jurisdiction has a tax treaty with the US. Currently approved jurisdictions: Panama, Dominican Republic, Costa Rica. All requests require VP approval. See the full policy here: [link].”
Architecture
Agentic RAG — what we deploy for production.
For enterprise workloads, a simple "search-then-answer" pipeline rarely holds up. We deploy Agentic RAG: multiple specialized agents that validate each other's work.
| Agent | Role |
|---|---|
| Retriever | Decomposes the query, performs iterative search |
| Critic | Validates faithfulness, checks for hallucinations |
| Compliance | Enforces regulatory boundaries and access controls |
| Formatter | Aligns output with enterprise standards |
If evidence is insufficient, the system searches again. If the answer is unreliable, it says so. That is the standard we hold production systems to.
Applications
Use cases by department.
Legal
Search contracts, regulations, case law, internal policies.
Finance
Query financial reports, forecasts, compliance documents.
Operations
Access SOPs, maintenance logs, equipment specs.
Human Resources
Search benefits policies, onboarding docs, compliance training.
Sales & Marketing
Query product specs, pricing history, competitor intelligence, past proposals.
Customer Service
Search knowledge base, troubleshooting guides, warranty terms.
Requirements
What you need.
Documents
They don't need to be perfect. PDFs, Word docs, spreadsheets, wikis, Confluence, SharePoint — we handle ingestion.
Access control
We configure role-based retrieval. Finance sees finance docs. Legal sees legal docs. Nobody sees everything unless they should.
A use case
One department, one document set, one question type. Start narrow, expand.
Contact
What questions should your AI be able to answer?
Pick one department. We'll build a RAG pipeline on your documents and show you what it can do.
Explore RAG for your team →