RAG and vector search over your own data, so answers come from verified sources your team can actually trust.
Ask a general-purpose model a question about your business and it'll answer anyway, filling the gaps with whatever pattern looks plausible. Retrieval-augmented generation stops that. We connect your model to a vector database, using vector search over embeddings of your own documents, records, and systems, so every answer is retrieved from something real before it gets written.
This is the capability behind semantic search, internal knowledge assistants and any AI feature that needs to answer from your content rather than the open internet. It sits close to the core of how we work: search and retrieval only perform well if the data underneath is clean, current, and structured for machines to query, which is the foundation work we do first.
Whether you're powering a support chatbot, a document assistant, or an internal search bar, the mechanics stay the same. Embed the content, index it for fast retrieval, and wire it into the model so it reaches for facts before it writes a word.
Every answer is retrieved from your verified content and systems, not the model's general training.
Vector databases built and tuned for fast queries, even across large, complex datasets.
The same retrieval layer can power search, chat, and summarisation, so you build it once and draw on it repeatedly.
Every record is versioned, validated, and embedded with lineage you can audit back to source.
Vector search finds information by meaning rather than exact keywords. Your content is turned into numerical embeddings and stored in a vector database; a query is embedded the same way, and the closest matches are retrieved. It's what lets a RAG system pull the right passage from thousands of documents in milliseconds.
A vector database stores your content as embeddings: mathematical representations that let a model search by meaning rather than exact keyword matches. Retrieval-augmented generation is what happens when that search feeds a language model: it retrieves the closest matching content, then writes its answer from that. We've written up both in plain terms, if you want the fuller picture: what a vector database is and how embeddings power AI search, and how RAG works for teams.
We identify which documents, records, and systems your AI actually needs to reach, and which it should stay well away from.
Text, tables, and metadata are converted into vector representations built for retrieval, not just storage.
We choose and configure the database against your performance, scale, and compliance needs, rather than defaulting to one vendor.
The vector store is wired into your LLM or API pipeline, so retrieval happens automatically before generation.
Observability, drift detection, and feedback loops keep the system accurate as your data and usage change.
Vector search finds information by meaning rather than exact keywords. Your content is turned into numerical embeddings and stored in a vector database; a query is embedded the same way, and the closest matches are retrieved. It's what lets a RAG system pull the right passage from thousands of documents in milliseconds.
RAG stands for retrieval-augmented generation. It pairs a vector database with a language model, so the model retrieves relevant, verified content before it answers, instead of relying only on what it learned during training.
Because it gives the model something real to check its answer against. A model that retrieves your verified content before writing an answer hallucinates far less than one working from memory alone.
No. We design for your data, performance, and governance needs first, then choose the vector store that fits, rather than defaulting to one vendor.
Yes. We integrate retrieval into your existing chatbots, internal tools, and API workflows rather than asking you to stand up a new destination.
Each system includes automated evaluation, embedding validation, and observability dashboards, so accuracy is monitored on an ongoing basis rather than assumed after launch.
Search and retrieval is usually the first capability worth building, because it proves the data foundation works before anything more ambitious gets layered on top. Talk to us about what your data would need to support it.
Tell us where your data is today and what you want AI to do. We'll come back with a straight answer on what your foundation needs and where the quickest real win is.
Talk to us