Fig AI Systems builds the data foundation AI agents run on: retrieval, semantic search and clustering that turn messy business data - documents, records, tickets, catalogs - into context an agent can actually use.
A vector database stores text, images or records as numeric vectors that encode meaning. Ask it for the items most similar to a query. It is a similarity index, not a reasoning engine.
A question phrased in one vocabulary can find material written in another. A support ticket about a charge appearing twice can surface a policy page about duplicate transactions, with no word in common. Similarity is not relevance, though. The design work sits in what gets indexed, how it is split and what metadata it carries.
Retrieval-augmented generation, usually shortened to RAG, is a four-step pattern. It grounds an answer in your own material.
RAG teaches the model nothing. It changes only what the model can see at the moment it answers. So fixing a wrong answer usually means fixing the index.
Retrieval is proposed far more often than it is warranted.
Clustering groups records by what they mean rather than by a field somebody remembered to fill in. A pile of tickets, documents or catalog entries becomes a structure an agent can navigate.
Clustering collapses near-duplicates that keyword matching misses. It discovers the categories a corpus actually contains. It isolates outliers worth a human look, and segments a large corpus so different agents can handle different parts. Report Grader applies the same idea in a stricter form. Skill-enabled agents are mapped to a domain-specific corpus, so grading is anchored to the review criteria that apply.
An agent with well-organized context and a narrow instruction beats a more elaborate agent reading an uncurated corpus.
Retrieval is a component, not a product. It earns its place when it makes a specific agent behave better.
The data layer is designed alongside the agent that consumes it, described on the custom AI agent development page. Where several agents share a corpus, the routing between them is designed on the multi-agent orchestration page. A retrieval change can improve one class of question while quietly breaking another. So every change is measured through closed-loop evaluation.
No. Many useful agents just read a record, call an API and write a result. Nothing about that requires embeddings. A vector database earns its place when an agent depends on material too large for every prompt and too unstructured to query. Retrieval that the workflow does not need buys an indexing pipeline and no extra correctness.
It reduces invention but does not remove it. Retrieval-augmented generation gives the model your own material to answer from. The model can still misread a passage, and retrieval can still return one that only looks relevant. Fig AI Systems therefore pairs retrieval with closed-loop evaluation that checks answers against their source passages.
A representative sample of the real material, and the questions people actually ask of it. Send the messy version. Mixed formats, near-duplicates and outdated records all change the design.
Describe the material your team relies on and what people ask of it. You will get a straight answer, including whether you need retrieval at all.
