Your AI gives confident wrong answers because it doesn't know your business?
Users can't trust AI responses because there's no way to verify them?
A language model trained on public data doesn't know your business. RAG (retrieval-augmented generation) fixes that -- by giving the model access to your documents, your database, and your internal knowledge at query time.
We build production RAG pipelines that index your data, retrieve the right context for each query, and generate accurate, source-backed responses. The result is an AI that knows your business because it can read your data -- not because it's guessing from training.
Full pipeline: ingestion, chunking, embedding, retrieval, and generation
Source citations on every response -- no hallucination without a paper trail
Works with PDFs, Word docs, databases, APIs, websites, and SharePoint
20+ RAG-powered products deployed to production
Language models are remarkable reasoners. They're poor fact-finders -- unless you give them the facts.
A language model trained on public data will answer questions about your proprietary products, your internal processes, and your customer history by either hallucinating plausible-sounding answers or admitting it doesn't know. Neither is acceptable in a production application.
RAG solves this by treating the model's knowledge and your data as separate concerns. The model provides reasoning and generation. Your data provides the facts. Every answer is grounded in something you can point to and verify.
If your use case is adding AI capabilities to an existing product rather than building a standalone pipeline, see our generative AI integration and ChatGPT application development services.
Extracting content from your sources -- PDFs, databases, APIs, websites -- cleaning and normalising it, splitting it into chunks of the right size for retrieval, and storing the source metadata alongside the content. The quality of ingestion determines the ceiling on retrieval quality.
Converting text chunks into high-dimensional vector representations that capture semantic meaning. We select the right embedding model (OpenAI, Cohere, or open-source) for your language and domain, and generate embeddings that maximise retrieval relevance for your query types.
Storing embeddings in a vector database with efficient approximate nearest neighbour search. Index configuration, metadata schemas, and filtering logic designed for your query patterns. We benchmark retrieval latency and tune index parameters for your scale requirements.
Taking a user query, embedding it, searching the vector store for relevant chunks, re-ranking the results, and assembling the context for the model. We implement hybrid search, query expansion, and metadata filtering to maximise retrieval precision for your specific use case.
Generating accurate, source-backed responses using the retrieved context. System prompts that instruct the model to cite sources, stay within the retrieved context, and indicate when a query falls outside the available knowledge. Structured response formatting for UI rendering.
Measuring RAG performance in production -- retrieval precision, answer accuracy, hallucination rate, and latency. Automated test sets for regression testing when you update the pipeline. Dashboards for monitoring query volume, retrieval patterns, and model costs.
Patterns we know work in production.
Index your internal documentation, policies, and SOPs. Give employees a search interface that answers questions directly -- sourced from your documents -- instead of returning a list of links. Legal teams searching contracts, support teams searching product docs, HR teams searching policy.
Index your product documentation, FAQs, and support history. Build a first-line support AI that resolves the 60% of questions that don't need a human -- with source citations so customers can verify. Human agents handle the complex cases that the AI escalates.
Index large document sets -- investment memos, legal filings, research reports, due diligence packs -- and query across them in natural language. M&A teams reviewing 500 documents for specific clauses. Legal teams searching across years of contracts for specific terms.
Tell us about your data sources and what you want the AI to answer. We'll design the RAG architecture and give you a fixed cost.
We audit your data sources -- what exists, where it lives, and how it's structured. We map the query patterns your users need to answer and define retrieval requirements. You get a scoped architecture document before a line of code is written.
Data source inventory and access review
Query pattern definition and test case creation
Vector store and embedding model selection
Fixed-cost project scope agreed upfront
Tell us your data sources, query volume, and use case. We'll design the architecture and give you a fixed cost.
I found RaftLabs to be the perfect partner for Perceptional, with their expertise in helping startup founders build MVPs, a free consultation, a prototype that matched my vision, and their unwavering support.
LLM Integration — Integrate a large language model into your application without building a full retrieval pipeline
Generative AI Integration — Add AI writing, summarisation, and Q&A capabilities to your existing product
ChatGPT Application Development — Custom applications built on GPT-4o and OpenAI APIs with your proprietary data
AI Document Intelligence — AI-powered reading and extraction from invoices, contracts, forms, and scanned records
Business Process Automation — End-to-end workflow automation with AI-powered exception handling
Tell us your data sources and query requirements. We'll design a RAG architecture and give you a fixed cost.
RAG is a pattern for building AI applications where the language model retrieves relevant information from your data before generating a response. Instead of relying on what the model learned during training -- which is general public knowledge -- the model searches your specific documents, database records, or knowledge base for content relevant to the query. It then uses that retrieved content as context when generating the response. The result is accurate, source-backed answers that reflect your actual information, not the model's guess.
Fine-tuning trains the model on your data so it learns patterns from it. RAG gives the model access to your data at query time. Fine-tuning is better for teaching the model how to respond (tone, format, terminology). RAG is better for making the model accurate about specific facts, recent information, and proprietary data that changes. Most production applications use RAG for factual accuracy and fine-tuning (or just good prompts) for behaviour. We'll recommend the right approach for your use case.
We've built pipelines for: PDF and Word documents, HTML and web content, databases (PostgreSQL, MySQL, MongoDB), SharePoint and Confluence, Google Docs and Drive, Notion, emails and support tickets, and custom APIs. The ingestion step extracts text from each source, processes it into chunks, generates embeddings, and indexes them in a vector store. We handle the extraction, transformation, and loading for each source type.
We've built RAG pipelines using Pinecone, Weaviate, Qdrant, PGvector (PostgreSQL extension), ChromaDB, and Supabase Vector. The choice depends on your infrastructure preferences, scale requirements, and whether you want managed hosting or self-hosted. For most applications, PGvector on a managed PostgreSQL instance is the simplest and lowest-cost option. We'll recommend the right vector store for your scale and operational requirements.
Retrieval quality is the hardest part of RAG. We improve it through chunking strategy tuned to your document types (semantic chunking vs. fixed-size vs. paragraph-level), hybrid search (combining dense vector search with sparse BM25 keyword search), re-ranking using cross-encoder models, query expansion and decomposition for complex questions, and metadata filtering so the model only searches relevant document subsets. We evaluate retrieval quality with precision and recall metrics against a test set of real queries before going to production.
A focused RAG pipeline -- one data source, one query pattern, one use case -- typically runs $25,000--$60,000 including the ingestion pipeline, the retrieval layer, and the generation and response formatting. A multi-source pipeline with custom retrieval strategies, evaluation frameworks, and production monitoring runs $60,000--$150,000. The cost depends on the number of data sources, the complexity of the retrieval requirements, and the scale of the system. Fixed cost, agreed before development starts.
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