Agentic RAG Agency. Ground Your AI in Your Own Knowledge.
We build retrieval-augmented generation systems that connect your AI agents to your documents, databases, and APIs. Your agents answer with your data, cite their sources, and eliminate hallucinations.
What Is Agentic RAG?
Agentic retrieval-augmented generation is the architecture that gives AI agents access to your knowledge base at runtime. Instead of relying solely on what the model was trained on, your agents retrieve relevant documents, chunks, or data from your systems and inject that context into every response. The result: answers that are grounded, sourced, and auditable.
Without RAG, an AI agent only knows what it was trained on. With RAG, it knows your product documentation, your customer history, your internal policies, and can cite exactly where every answer came from. That is the difference between a demo agent and one you trust with your business.
The RAG Pipeline
Ingestion
Documents, PDFs, web pages, and database records are parsed, cleaned, and prepared for indexing.
Chunking
Content is split into semantically meaningful chunks with overlap, balancing context preservation and retrieval precision.
Embedding
Each chunk is converted to a vector embedding via an embedding model. These vectors capture semantic meaning.
Retrieval
User queries are embedded and matched against the vector store. Top-k similar chunks are retrieved with similarity scoring.
Generation
Retrieved chunks are injected into the LLM prompt as context. The model generates a grounded, sourced response.
Ingestion → Chunking → Embedding → Retrieval → Generation
Five steps from raw documents to sourced, auditable AI responses. Your agents cite your data, not their training set.
Book Strategy CallWhy We Build With RAG
Eliminate Hallucinations
When an agent answers from your documentation, not its training data, the answer is grounded in fact. RAG with source citation makes every response auditable.
Keep Knowledge Current
Update your knowledge base, not the model. New product docs, policy changes, or market data are available to the agent immediately. No retraining required.
Access-Controlled Knowledge
Different agents, different knowledge. A support agent sees help docs. A legal agent sees contracts. RAG with permission scoping means agents only retrieve what they are authorized to see.
Cost-Effective at Scale
Embedding and retrieval are orders of magnitude cheaper than fine-tuning. You can index millions of documents and query them for fractions of a cent per retrieval.
What We Build With RAG
Knowledge Base Agents
Customer support agents that answer from your docs, help center, and internal wiki, with source citations for every answer.
Document Q&A Pipelines
Automated pipelines that ingest contracts, invoices, and case files, then answer natural language queries about their contents.
Real-Time Enrichment
Agents that pull live data from your CRM, database, or API during conversation, enriching responses with current customer context.
When RAG Is the Right Tool
Use RAG When
- •Knowledge changes frequently
- •Responses need source citations
- •Data volume exceeds context window
- •Multiple agents need shared knowledge
- •Access control per document is required
Use Fine-Tuning When
- •You need consistent tone/style
- •The domain is narrow and stable
- •Latency must be minimal
- •Knowledge rarely changes
- •The model IS the product
Consider Alternatives
- •Pure prompt engineering for simple facts
- •Full-text search for keyword matching
- •Graph databases for relationship queries
- •Caching for frequently repeated Q&A
- •Hybrid: RAG + fine-tuning for max accuracy
Explore Related Services
Frequently Asked Questions
How is RAG different from just using an LLM?▾
What data sources can we connect?▾
How does the system stay current when our docs change?▾
Can this run self-hosted for data privacy?▾
What does a typical project cost and how long does it take?▾
Ship RAG Systems That Your Agents Actually Trust
Let us architect a RAG pipeline that connects your agents to your knowledge base, with source citations, access controls, and production reliability from day one.
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How to orchestrate multiple AI agents that share a retrieval layer and answer complex queries together.
Vector Databases for RAG: An n8n Comparison
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