RAG development services from BigData Boutique cover the whole system, not just the prompt: document ingestion and chunking, hybrid vector and keyword retrieval, reranking, generation, evaluation, guardrails, and the production deployment that keeps it accurate as your data changes. We build retrieval-augmented generation on your data, in your cloud, on top of the search engines we have run in production for fifteen years.
Most RAG projects that fail do not fail at the model. They fail at retrieval: the right passage is not in the index, is chunked badly, or ranks below ten irrelevant ones. That is a search problem, and search is what we do. If you are new to the pattern, start with what RAG is.
Key facts
- We design and build RAG pipelines on Elasticsearch, OpenSearch, and vector databases, with Amazon Bedrock, Anthropic Claude, OpenAI, and open-source models.
- Every RAG engagement ships with an evaluation set and a retrieval quality baseline, so improvements are measured rather than guessed.
- Engagements run as a fixed-scope project, an hours bank, or a forward deployed engineer embedded with your team.
- The AI Launchpad program takes a RAG use case from idea to production in weeks.
What Our RAG Development Services Include
Retrieval design and hybrid search
The index is the product. We design chunking and metadata strategies for your documents, choose between dense, sparse, and hybrid retrieval based on measured recall on your queries, and add reranking where it pays for itself. Our semantic search solutions cover the retrieval layer in depth.
Ingestion and data pipelines
Connectors to your document stores, wikis, ticketing systems, and databases, with change data capture so the index stays current. Access control is carried through to retrieval so users only see what they are allowed to see. This is data engineering, and it is usually most of the work.
Generation, prompting, and guardrails
Prompt and context assembly, citation of sources, refusal and fallback behavior, PII handling, and the guardrails that stop a knowledge assistant from inventing a policy. We work with Bedrock Guardrails, model-native controls, and custom checks.
Evaluation and observability
A labeled evaluation set built from your real questions, retrieval metrics (recall, MRR) and answer metrics (faithfulness, relevance) tracked per release, plus tracing in production so you can see why an answer was wrong. See our note on LLM evaluation as part of LLM development.
Agentic and GraphRAG patterns
When single-shot retrieval is not enough: multi-step retrieval, tool use, query decomposition, and GraphRAG over entity relationships, built with our AI agent development practice.
Deployment and cost control
Serverless or containerized deployment in your AWS, GCP, or Azure account, caching, model routing, and token budgets, so the system that worked for ten users still works, and is still affordable, for ten thousand.
RAG Projects We Have Delivered
- Max Security built SCOUT AI with us: an intelligence assistant on OpenSearch, Amazon Bedrock, and Anthropic Claude that cut analyst briefing time by 79 percent. Read the case study.
- ScreenSteps worked with us on a hybrid search framework combining Elastic Cloud, Amazon Bedrock, Nova, and Cohere that sharply reduced failed searches. Read the case study.
How a RAG Engagement Works
- Scoping call. Thirty minutes on your documents, users, and the questions the system must answer. You meet the engineer who will lead the work.
- Retrieval baseline. A one- to two-week assessment that indexes a sample of your data, builds a first evaluation set, and reports where retrieval stands today.
- Build. Ingestion, retrieval, generation, and guardrails built in your cloud, with evaluation run on every change.
- Launch and hand over. Production deployment, tracing, runbooks, and knowledge transfer, with optional production support for the search layer.
See how our engagement models and pricing work for the options.
Frequently Asked Questions
What are RAG development services?
RAG development services cover designing, building, and operating a retrieval-augmented generation system: ingesting documents, indexing them for hybrid search, retrieving the right context for each question, generating grounded answers with an LLM, and evaluating and monitoring the result in production.
How long does it take to build a production RAG system?
A working baseline on a sample of your data takes one to two weeks. A production system with ingestion, access control, evaluation, and deployment typically takes six to twelve weeks depending on the number of data sources. The AI Launchpad program runs this on a fixed path.
Which vector database should we use for RAG?
Often the one you already run. Elasticsearch and OpenSearch both offer production vector search with hybrid ranking, and for most enterprise RAG workloads they outperform a separate vector database once filtering, access control, and keyword matching are included. We are vendor-neutral and will show you the retrieval numbers for your data before recommending.
How is RAG development priced?
Retrieval baselines and defined builds are quoted as fixed-scope projects. Ongoing improvement runs on an hours bank, and longer builds can use a forward deployed engineer embedded with your team. We do not publish list prices; you receive a written quote before any work is billed.
Can you fix a RAG system we already built?
Yes, and it is one of our most common engagements. We start with an evaluation set from your real queries, measure retrieval and answer quality, and fix the biggest gaps first, which are usually chunking, hybrid ranking, and missing metadata rather than the model.
Does our data leave our environment?
No. Ingestion, indexing, retrieval, and generation run in your cloud account, with the model provider you choose. Nothing is copied to BigData Boutique systems.