LLM giving plausible but wrong answers from your product documentation?
Employees asking the same questions because the AI can't find the right policy?
LLMs hallucinate when they don't know the answer. For general knowledge questions, that's manageable. For questions about your product, your policies, your contracts, or your procedures, it's a liability. A model trained on the internet doesn't know what your product does, what your SLA says, or what your compliance policy requires.
Retrieval-augmented generation (RAG) fixes this. Instead of relying on training data, the model retrieves the right document from your knowledge base before generating a response. It answers from your content, with citations, not from its best guess.
LLM responses grounded in your documents and knowledge base
90%+ retrieval accuracy on domain-specific knowledge across our deployments
15+ RAG systems built across support, compliance, and enterprise knowledge use cases
Fixed project cost -- scoped before development starts
A language model trained on public data knows a lot about the world in general. It knows almost nothing about your product, your contracts, your procedures, or your customers. When you ask it about your specific context, it fills the gap with plausible-sounding text from its training data -- which is often wrong in ways that are hard to detect.
RAG changes this. Instead of generating from training data, the model retrieves the specific documents relevant to your question and generates a response from that content. If your policy document says one thing and the model's training data suggests another, the model uses your document. The response is accurate to your knowledge, not the internet's.
This matters most in high-stakes contexts: customer support (wrong policy information damages trust), legal and compliance (wrong clause interpretation creates liability), healthcare (wrong clinical information creates risk), and internal operations (wrong procedure information causes errors).
Internal search tools that let employees ask questions in natural language and get accurate, cited answers from your company's documentation, policies, runbooks, and wikis. Replaces endless folder navigation and keyword search with a query interface that understands intent.
Systems that answer questions from specific document sets -- contracts, compliance manuals, technical specifications, research reports. Users ask questions; the system returns precise answers with the exact source passage cited.
RAG-powered support systems that give customer support agents or chatbots accurate, citation-backed answers from your product documentation and support knowledge. Agents get the right answer immediately; chatbots can resolve queries without hallucinating.
RAG pipelines that retrieve from multiple sources simultaneously -- documents, databases, APIs, and real-time data -- and synthesise a coherent response. For complex queries that require cross-referencing multiple knowledge sources.
Systems that answer compliance questions accurately from regulatory documents, internal policies, and audit records. Built for finance, healthcare, legal, and other regulated industries where accuracy is not optional.
RAG systems over codebases, API documentation, and technical runbooks. Developers ask questions about how your system works and get accurate answers grounded in the actual code and documentation -- not generic Stack Overflow answers.
Tell us the knowledge sources and the query types. We'll design the RAG architecture and give you a fixed cost.
We extract content from your data sources, clean and chunk it, generate embeddings, and index it in a vector store. The chunking strategy and embedding model are chosen based on your content type and accuracy requirements.
When a query comes in, we retrieve the most relevant chunks, apply re-ranking to improve precision, and assemble the context for the generation step. We optimise the retrieval pipeline specifically for your query types.
The LLM generates a response grounded in the retrieved context. We add source attribution, confidence scoring, and fallback logic. If retrieval quality is low, the system surfaces that rather than generating uncertain output.
Generative AI Development -- Full LLM application development beyond retrieval
AI Chatbot Development -- Chatbots powered by RAG for accurate customer-facing responses
AI Agent Development -- Agents that use RAG as their knowledge layer
AI Document Intelligence -- Extracting structured data from unstructured documents
LLM Integration -- Connecting LLMs to your existing products
Tell us what your RAG system needs to know. We'll design the architecture and give you a fixed cost.
Frequently asked questions
RAG is an architecture where a language model retrieves relevant context from a knowledge base before generating a response. Instead of relying on what the model learned during training, it reads the specific documents, passages, or records that are relevant to the question -- and generates a response grounded in that content. The result is accurate, citation-backed answers from your specific knowledge, not hallucinated outputs from the model's general training.
Use RAG when your knowledge changes frequently, when accuracy and citations are critical, or when your knowledge base is too large to fit in context. Fine-tuning is better when you need to change the model's tone or style, teach it a specific format, or improve performance on a narrow task. For most enterprise knowledge applications -- internal search, customer support, document Q&A -- RAG gives better accuracy at lower cost than fine-tuning, and updates to the knowledge base don't require retraining.
We connect RAG systems to documents (PDFs, Word files, HTML), databases (SQL, NoSQL), ticketing systems (Zendesk, Jira), wikis (Confluence, Notion), SharePoint, Slack, email, and custom data stores. We handle the extraction, chunking, embedding, and indexing pipeline for each source type. If your data is in a structured format we haven't mentioned, we can write a custom connector.
The core RAG architecture grounds responses in retrieved context, which eliminates most hallucination. We add further guardrails -- confidence scoring on retrievals, fallback responses when retrieval quality is low, source attribution in every response, and conversation monitoring that flags anomalous outputs. We also test accuracy against a set of ground-truth question-answer pairs before launch. If the retrieval doesn't find relevant context, the system says so rather than guessing.
A focused single-domain RAG system -- connecting one or two knowledge sources and building a query interface -- typically takes 4--8 weeks. A multi-domain enterprise RAG system with custom connectors, access controls, and an analytics dashboard takes 10--16 weeks. We build a working demo in the first 2 weeks so you can test accuracy before committing to the full scope.
A focused RAG system for a single use case typically runs $15,000--$40,000. A multi-domain enterprise RAG system with custom connectors and a full product interface typically runs $45,000--$120,000. Cost depends on data source complexity, the number of domains, access control requirements, and whether you need a custom UI or API-only access. We scope every project before pricing it.
Yes. We implement document-level access controls so users can only retrieve content they're authorised to see. This is critical for enterprise deployments where the knowledge base contains content with different access tiers -- HR documents visible only to managers, client-specific content visible only to the relevant account team, or regulated data with compliance restrictions. The access control layer is designed as part of the retrieval architecture, not bolted on after.