RAG Development Services

Retrieval-Augmented Generation connects a language model to your enterprise data at query time. The user asks a question. The system retrieves relevant information from your documents, databases, and APIs. The model generates a response grounded in that retrieved context. The result: AI that answers from your verified data rather than its training data - reducing hallucinations by 70-90%, accessing private information no public model has, and staying current without retraining. 72% of enterprises now run RAG in production, making it the default architecture for enterprise AI in 2026.

Almost always retrieval quality. Demos work on 50 clean documents where every query has an obvious match. Production breaks on ambiguous queries, documents that span multiple topics, information split across chunk boundaries, stale documents that haven't been re-indexed, and permission mismatches. The fix is retrieval engineering: semantic chunking (up to 70% better accuracy), hybrid search (catches what vector-only misses), reranking (ensures the top result is the right result), and continuous evaluation (catches degradation). The model is usually fine. The retrieval usually isn't.

RAG supplies knowledge at query time from your data - facts, policies, documents. Fine-tuning permanently changes how the model behaves - its format, tone, vocabulary. RAG handles what changes (policies update, prices shift, regulations evolve). Fine-tuning handles what shouldn't change (output schema, domain terminology, brand voice). Most production systems in 2026 use both: RAG for facts, fine-tuning for form. Our LLM Fine-Tuning Services page covers the other half of this architecture.

Semantic chunking splits documents at meaning boundaries - paragraph breaks, section ends, logical units - rather than fixed token counts (e.g., every 512 tokens). A legal clause split mid-sentence retrieves the wrong half. A medical protocol split between its condition and its action retrieves an incomplete answer. Semantic chunking delivers up to 70% better retrieval accuracy in benchmarks. It's the single highest-leverage engineering decision in the RAG pipeline, and the one most teams set once as a fixed-size default and never revisit.

Graph RAG combines vector retrieval with a knowledge graph - structured relationships between entities in your data. Standard RAG retrieves text chunks. Graph RAG retrieves relationships: "company X has product Y that complies with regulation Z." This matters for multi-hop questions that require traversing connections - which products meet which standards, which policies apply to which departments, which drugs interact with which conditions. Standard RAG struggles with these because the answer spans multiple chunks that aren't textually similar.

Three metrics, continuously. Faithfulness: is the generated answer grounded in the retrieved context? Context precision: was the retrieved context actually relevant to the question? Retrieval recall: did we find all the relevant chunks? Production thresholds for customer-facing systems: faithfulness above 0.85, context precision above 0.75. We build evaluation infrastructure that measures these on every query - not once at launch. RAG systems that aren't evaluated systematically degrade silently over time.

The RAG system inherits permissions from each source application via your identity provider (Okta, Entra ID, Google Workspace). At query time, retrieval is filtered so only documents the user is authorised to access contribute to the answer. This isn't a post-generation filter - it's enforced during retrieval, so the model never sees unauthorised content. Without this, your RAG system becomes an accidental data leak: ask the right question and receive information from a document you shouldn't have access to.

Cost tracks data source count, document volume, retrieval tier (Advanced, Graph, Agentic), evaluation depth, permission complexity, and deployment model. A focused RAG pipeline on 3-5 data sources with Advanced retrieval is a moderate build. Graph RAG with knowledge graph construction adds significant scope. Agentic RAG with multi-step reasoning adds more. The most expensive component is often data preparation - cleaning, structuring, and chunking enterprise documents that were never designed to be machine-readable. We scope fixed pricing after the data audit.

Yes - and this is often the highest-ROI engagement. Most underperforming RAG systems have the same problems: fixed-size chunking losing context at boundaries, vector-only search missing exact-match queries, no reranking (so the top result isn't the best result), no evaluation infrastructure (so degradation goes undetected), and stale documents still being retrieved. We audit the pipeline end-to-end, identify where information is lost, and recommend specific fixes - which are often engineering improvements to the existing system rather than a full rebuild.

When the answer doesn't depend on your data - general knowledge questions, creative writing, brainstorming. When the document set is small enough to fit in the model's context window (though this becomes cost-prohibitive at scale). When the problem is model behaviour rather than model knowledge - consistent output format, domain vocabulary, or tone issues are fine-tuning problems, not RAG problems. And when data quality is too poor for retrieval to help - RAG on a poorly organised, outdated, contradictory knowledge base produces grounded but unreliable answers. Fix the data first.