RAG implementation services: ground your LLM in your own data

When is RAG the right answer, and when is it not?

RAG is the right answer when the model needs facts it was never trained on, when those facts change, and when users need to see where an answer came from. It is the dominant production pattern for exactly that reason: according to Menlo Ventures, November 2024, 51 percent of enterprise production deployments used retrieval-augmented generation, up from 31 percent the year before, while only 9 percent of production models were fine-tuned.

RAG is the wrong answer when the problem is behaviour rather than knowledge. If you need a consistent tone, a strict output format or lower latency on a narrow task, a fine-tune (often LoRA or QLoRA on an open-weight model) does that better, and the two can be combined. If the job is to take actions across systems rather than answer questions, you need an AI agent, which will usually use retrieval as one of its tools. Our guide to RAG vs fine-tuning covers the decision in depth.

How do you build a RAG pipeline that stays accurate?

Accuracy comes from the retrieval layer, so that is where the engineering effort goes. The sequence we follow: audit the data, establish a retrieval-quality baseline with the simplest pipeline that works, build an evaluation set of real questions, then improve one variable at a time until the numbers justify production, and keep measuring after launch because documents, users and models all drift.

• Chunking and metadata: chunk by document structure rather than fixed length, keep titles, sections and dates as metadata, and test chunk size against the evaluation set.
• Hybrid retrieval and reranking: combine BM25 keyword search with dense vectors, then rerank the top candidates with a cross-encoder; this is usually the single largest accuracy gain.
• Stack: pgvector when your data already lives in Postgres, Qdrant or Weaviate for dedicated vector workloads, Pinecone when you want it fully managed; LlamaIndex or LangChain for orchestration; Databricks or Snowflake as structured sources.
• Generation: cite sources, refuse when retrieval confidence is low, and keep prompts versioned so a regression can be traced to a change.

How do enterprise teams handle access control, PII and on-prem data?

Permissions must be enforced at retrieval time, not in the prompt. Each chunk carries the access metadata of its source, and the query is filtered by the identity of the person asking before anything reaches the model. PII is redacted or tokenised at ingestion where policy requires it, and every request logs what was retrieved, what was shown and which model produced the answer. When data cannot leave your environment, the whole pipeline, including an open-weight model served with vLLM, runs inside your cloud account or on-premise. These decisions are made in the data audit, before a line of pipeline code is written.

What does a RAG engagement look like, and what drives the cost?

For a single well-defined corpus, one senior RAG engineer working inside your codebase is usually enough. When sources are scattered across many systems, permissions are complex or structured data from a warehouse is part of the answer, we pair the RAG engineer with a data engineer as a two-person pod. Both shapes come with a dedicated account manager and can scale up or down as the scope changes.

Cost is driven by the number and messiness of sources, how strict the permission model is, the accuracy bar and how it must be proven, whether the model must run on your infrastructure, and how much connector and re-indexing maintenance the corpus needs over time. Volume of queries matters less than people expect; volume of documents and the rate at which they change matter more. We scope these before the trial and give you a clear estimate. For the people side, see our guide to what it costs to hire AI engineers.