RAG development services
Your AI answers questions wrong because it doesn't know your business. Retrieval-augmented generation fixes that by connecting large language models to your actual data: documents, databases, knowledge bases, support tickets, whatever you've got.
We build RAG systems that give accurate, sourced answers instead of confident guesses.
SportHub
Flustr
Armor Up America
Rundivo
RAE HealthWhat is retrieval-augmented generation?
RAG is a pattern where an AI model pulls relevant information from your own data before generating a response. Instead of relying only on what the model learned during training (which may be outdated or too general), the system first searches your documents, then uses those results as context for the answer.
You get responses grounded in facts you control, with sources you can verify.
The mechanics are straightforward. A user asks a question. The system converts that question into a vector embedding and searches your vector database for the most relevant chunks of text. Those chunks get passed to the LLM alongside the original question. The model generates an answer using your data as its source material, and cites where it found each piece of information.
Without RAG, a standard LLM will confidently answer questions about your product, your policies, or your internal processes, and be completely wrong. RAG keeps the model honest.
What we build with RAG
Enterprise knowledge base search
Your company has thousands of pages of documentation scattered across Confluence, SharePoint, Google Drive, and internal wikis. Nobody can find anything. We build RAG-powered search that actually understands questions and pulls the right answer from the right document, even when the user doesn’t know the exact terminology.
Customer support chatbots with RAG
A support chatbot is only useful if it gives correct answers. We build RAG chatbots that answer questions from your help docs, product manuals, and ticket history. When the bot doesn’t know something, it says so instead of making things up.
AI agents for customer service: beyond the chatbot→Internal Q&A tools for teams
Legal wants to search contract templates. HR needs answers from the employee handbook. Engineering wants to query past postmortems. Same RAG architecture, different data sources. We build internal tools that let your teams ask natural-language questions and get answers with page references.
RAG-powered product features
Some of our clients embed RAG directly into their own products. A SaaS platform with AI-powered documentation search. A healthcare app that helps clinicians find treatment protocols. A legal tool that surfaces relevant case law. We handle the RAG pipeline so you can focus on the product.
Document analysis and extraction
When you need to process large volumes of contracts, reports, or regulatory filings, RAG combined with structured extraction can pull specific data points, flag anomalies, and summarize findings. More reliable than an LLM alone, because every answer traces back to a specific passage in a specific document.
Multi-source RAG systems
Most real-world RAG projects pull from more than one data source. A database here, a CRM there, a pile of PDFs, and an API or two. We build multi-source RAG architectures that unify these into a single retrieval layer, so the model can cross-reference your CRM data with your documentation and your support tickets in one query.
How we build a RAG pipeline
Data audit and preparation
RAG is only as good as the data behind it. We start by cataloging your data sources, assessing their quality, identifying gaps, and deciding how to chunk and index them. Messy data produces messy answers, so we spend real time here.
Data readiness: the hidden requirement for AI success→Embedding and indexing
We convert your content into vector embeddings and store them in a vector database (Pinecone, Weaviate, Qdrant, pgvector, or Milvus, depending on your scale and budget). This is where the technical choices matter: chunk size, overlap strategy, embedding model selection, and metadata tagging all affect retrieval quality.
Retrieval pipeline design
The retrieval step is the most underestimated part of RAG. We build hybrid retrieval systems that combine vector search with keyword search, re-ranking, and filtering. Simple vector similarity often falls short on its own. You need strategies like query decomposition, hypothetical document embeddings (HyDE), and parent-child chunking to get consistently good results.
LLM orchestration
We integrate the retrieval pipeline with your chosen LLM (GPT-4, Claude, Llama, Mistral, or others) and handle prompt construction, context window management, citation formatting, and fallback logic. The model should give accurate answers with sources, and it should know when it doesn’t have enough context to answer at all.
Evaluation and tuning
We test the system against your actual questions and edge cases. RAG evaluation isn’t straightforward. You need to measure both retrieval quality (did we find the right documents?) and generation quality (did the answer actually use them correctly?). We use frameworks like RAGAS and custom test suites to measure both.
Deployment and monitoring
A RAG system in production needs monitoring. We track retrieval hit rates, answer quality scores, latency, token costs, and user feedback. When your data changes (and it will), the system needs to re-index and stay current. We set up the infrastructure for all of this.
RAG vs fine-tuning: which approach do you need?
This is the most common question we hear from clients evaluating AI projects, and the answer is usually “RAG first, fine-tune later if needed.”
| RAG | Fine-tuning | |
|---|---|---|
| Best for | Answering questions from specific, changing data | Teaching the model a new style, format, or domain vocabulary |
| Data freshness | Answers reflect your latest data — just re-index | Frozen at training time — retrain to update |
| Transparency | Cites sources, answers are traceable | Answers come from model weights — no citations |
| Cost to start | Lower — no GPU training required | Higher — compute-intensive training runs |
| Hallucination control | Stronger — model is grounded in retrieved context | Weaker — model can still fabricate |
| When to combine | Use RAG for retrieval + fine-tuned model for better generation quality | |
For most business applications, RAG is the right starting point. It gives you factual answers with citations, works with your current data, and doesn't require training infrastructure. Fine-tuning makes sense later if you need the model to write in a specific voice, follow a particular output format, or handle a specialized domain where the base model struggles with terminology.
We help you figure out which approach (or combination) fits your situation. That's part of what RAG consulting looks like in practice: not just building the pipeline, but deciding whether to build it at all.
Our RAG technology stack
LLM providers
OpenAI (GPT-4, GPT-4o), Anthropic (Claude), Meta (Llama), Mistral, Google (Gemini), open-source models via Ollama
Vector databases
Pinecone, Weaviate, Qdrant, Milvus, pgvector (PostgreSQL), ChromaDB, Elasticsearch with vector search
Orchestration frameworks
LangChain, LlamaIndex, Haystack, custom Python pipelines
Embedding models
OpenAI text-embedding-3, Cohere Embed, open-source models (sentence-transformers, BGE, E5)
Infrastructure
AWS (Bedrock, SageMaker, Lambda), GCP (Vertex AI), Azure (OpenAI Service), on-premise deployment where required
Evaluation
RAGAS, DeepEval, custom evaluation frameworks
Data connectors
Confluence, SharePoint, Google Drive, Notion, S3, databases (PostgreSQL, MySQL, MongoDB), REST APIs, Salesforce
RAG applications by industry
Healthcare
Clinicians searching treatment protocols, drug interaction databases, and clinical guidelines. RAG keeps answers grounded in peer-reviewed sources and institutional policies. No hallucinated medical advice.
Applications of machine learning in healthcare→Financial services
Compliance teams querying regulatory documents. Traders searching research reports. Advisors pulling client-relevant market intelligence. Financial data is dense and it changes constantly, which makes it a poor fit for a model that was trained six months ago. RAG keeps pace.
Legal
Searching case law, contract databases, and regulatory filings. RAG gives lawyers citations they can verify, not summaries they have to fact-check from scratch.
E-commerce
Product catalog search that understands "running shoes for flat feet under $100" rather than matching keywords. RAG connects your product data with customer reviews and specification sheets.
SaaS and technology
In-app documentation search, onboarding assistants, and admin tools that answer questions about your own platform. Your support team will thank you.
Why work with Attract Group on RAG development
We’ve been building custom software since 2011.
RAG is new; software engineering isn’t. We know how to ship production systems that work reliably, handle edge cases, and scale. That experience matters when your RAG pipeline needs to process 100,000 documents and serve answers to 500 concurrent users.
We tell you when RAG isn’t the answer.
Not every AI problem needs retrieval-augmented generation. If your use case is better solved with a fine-tuned model, a structured search engine, or plain old database queries, we’ll say so. We don’t sell you a RAG system because that’s what you Googled.
You own the code.
Everything we build is yours. No proprietary wrappers, no vendor lock-in. If you want to take the system in-house after launch, you can.
We build the whole thing.
A RAG pipeline doesn’t exist in a vacuum. It needs a data layer, an API, a frontend, authentication, monitoring, and deployment infrastructure. We handle the complete system, not just the AI part.
We’ll tell you where RAG falls short.
RAG has real limitations. It can struggle with numerical reasoning, it requires clean data, and retrieval quality depends heavily on how you chunk your documents. We’ll be upfront about these tradeoffs before you commit.
What clients say
Frequently asked questions about RAG development
Start with a RAG consultation
Tell us about your data and your use case. We'll give you an honest assessment of whether RAG is the right approach, what it'll take to build, and what results you can expect. No pitch deck, no hard sell. Just a technical conversation with engineers who've done this before.
Ready to build a RAG system?
Tell us about your data sources and use case. We'll respond with an honest assessment and realistic timeline.