Find the most relevant document for any search term

Type a keyword and AI scores your whole document library to surface the single best-matching document.

How the work actually flows

A straight line. Runs once per one per document in library.

Pattern: Sequence (1) ยท Multiple Instances with a priori Design-Time Knowledge (13)

flowchart TD trig(("you submit a keyword")):::human s0["convert keyword to vector"]:::svc s1[["score against each document"]]:::mi s2["average and rank scores"]:::task s3["return best match"]:::task trig --> s0 s0 -->|"one per one per document in library"| s1 s1 --> s2 s2 --> s3 out[/"top matching document identified"/]:::out pay{{"stop guessing which file to open"}}:::pay s3 --> out out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
A stepAn outside serviceRuns once per itemA personResultPayoff
Build size
Advanced

A larger build with multiple systems, AI reasoning, and custom rules.

Business functions
Knowledge Base & RAG
Connects
QdrantOpenAI

The problem it solves

You have a growing library of reports, papers, or files, and finding the one that actually covers a specific topic means opening and skimming document after document. Keyword search in a folder often turns up nothing useful because the wording doesn't match exactly. You end up guessing which file is worth your time.

Who it fits

Research teams, consultants, or knowledge-heavy businesses managing a large document library.

How it works

  1. You submit a keyword or topic you're searching for
  2. OpenAI converts the keyword and your documents into searchable vectors
  3. Qdrant compares the keyword against every document and scores the matches
  4. Scores are averaged and grouped by document
  5. You get back the document that best matches your search, ranked by relevance
What you get

The right document surfaced instantly from your library

You type a keyword and get back the single document in your library that best matches what you're looking for.

What you get

A ranked result showing which document in your library best matches your search term.

What you need

A Qdrant vector database and an OpenAI API key.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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