We build AI systems trained on your internal documentation, policies, and institutional knowledge - giving your team source-cited answers in seconds instead of hours.
Retrieval-Augmented Generation (RAG) is a technique where an AI model answers questions by first retrieving relevant documents from a specific knowledge base, then generating a response grounded in those specific documents. The critical difference from a standard AI chatbot is that the answers come from your materials - not from general training data. Every response includes citations pointing to the exact source document and section it drew from.
The result is an AI that can accurately answer questions about your policies, your processes, your historical work, and your institutional knowledge - and show its sources. A new hire can ask a question and get the answer from the correct policy document rather than a colleague's best recollection. A team member preparing a proposal can surface relevant past work in seconds rather than spending an hour searching through Drive folders.
Institutional knowledge is scattered. New employees ask the same questions that senior staff have answered a hundred times. Policy documents live in a folder that nobody can find because it was last updated in 2021 and has three different versions. Standard operating procedures exist in theory but are 40 pages long, not easily searchable, and nobody reads them. The onboarding process relies on a senior person being available to answer questions for two weeks.
A RAG system makes all of that instantly accessible to anyone who needs it. The knowledge still lives in your systems - the RAG layer simply makes it queryable by anyone, in natural language, with exact source references so the answer can be verified and trusted.
We ingest content from wherever your knowledge currently lives. Most deployments pull from a combination of sources - the audit phase of every engagement maps where the relevant documentation is and what format it is in, before we design the ingestion pipeline.
We ingest your documents into a vector database. The choice of database - Pinecone, FAISS, or Qdrant - depends on your scale, infrastructure requirements, and whether the system needs to run in a managed cloud environment or on your own servers. Each document is chunked, embedded, and stored in a way that makes semantic retrieval fast and accurate.
When a user asks a question, the system retrieves the most relevant content chunks from the vector database, then passes them to Claude as the generation layer. Claude synthesises an answer from the retrieved content, with citations to the specific documents it drew from. Staff interact with the system through a chat interface, a Slack bot, or an embedded widget in your existing tools - whichever integration fits your team's workflow.
RAG systems are only as good as their retrieval accuracy. A well-designed vector database with poorly tuned retrieval parameters will surface the wrong documents, and the generated answer will be wrong regardless of how good the generation model is. We spend significant time in the retrieval tuning phase: chunk size and overlap, embedding model selection, retrieval threshold calibration, and re-ranking logic for queries that return multiple competing results.
We also build evaluation pipelines for every production deployment. These run a defined set of test questions - questions with known correct answers from specific source documents - against the system before and after any configuration changes. If retrieval accuracy falls below threshold, we are alerted before it affects users. This is not a one-time setup; it is an ongoing quality layer that runs on a schedule.
The use cases where RAG knowledge systems deliver the clearest return span the full employee lifecycle and several client-facing functions.
The businesses that benefit most are those where the same questions are asked repeatedly by different people, where the answers exist but are hard to find, and where the cost of the wrong answer - in onboarding time, compliance risk, or proposal quality - is meaningful enough to justify an investment in making the right answer reliably accessible.