Answers
What is vector search?
In short
Vector search retrieves documents by semantic similarity, using embeddings that capture meaning rather than exact word matches — the retrieval engine behind most RAG systems.
Short answer
Vector search retrieves documents by semantic similarity, using embeddings that capture meaning rather than exact word matches — the retrieval engine behind most RAG systems.
What this actually means in practice
Choose an embedding model fit for your domain; chunk documents at boundaries that preserve meaning; use a vector store (Pinecone, Qdrant, pgvector) sized for your corpus; and add re-ranking for high-stakes queries.
The most common pitfall
Default chunking and default embeddings — and wondering why retrieval feels mediocre.
What to do next
Build a small eval set and measure retrieval precision before scaling the corpus.
Frequently asked questions
Does Forth Systems help with this?
Yes — Forth Systems works with banks, payment institutions, insurers and infrastructure operators on exactly this kind of work. Engagements start with a fixed-scope assessment so you see the shape before committing.
How experienced is the team?
Engagements are staffed by named, UK-based senior engineers — not a rotating offshore pool. References from the second line of comparable clients are available on request.
Where are you based?
Edinburgh-based, delivering UK-wide with onsite presence in London and across Scotland as required.
How fast can we start?
Most engagements start within 2-4 weeks of a signed SoW, faster where an existing supplier framework is in place.