From Documents to Findings
Everything in Classifyre starts with turning documents you already have into signals worth acting on. This page walks through that first stretch, in plain terms.
Sources: the systems you connect
A source is a connection to a system you already run — SharePoint, Confluence, Jira, a file share, a local folder, and more. Connecting a source doesn’t move your data anywhere; Classifyre reads it in place, on the schedule and scope you choose.
Scans: one pass over a source
A scan is a single crawl of a source. You can scan on demand or on a schedule, and each scan is recorded so you can see its progress and history. Repeat scans are smart about it: unchanged items aren’t reprocessed, changed items are refreshed, and items that disappeared from the source have their findings automatically closed out. Nothing is silently duplicated or lost.
Assets: what a scan finds
Every document, file, page, or record a scan discovers becomes an asset — along with metadata like its owner, location, and last-changed date. Assets are the unit everything else attaches to: findings, evidence, connections.
Detectors: what looks for what
A detector reads an asset’s content and looks for something specific. There are three kinds:
- Pattern-based — precise rules for structured things like credentials or ID numbers.
- Machine-learning — models that recognise entities like names or organisations even when the wording varies.
- Custom, AI-based — detectors you define, including ones backed by an AI model, for anything specific to your investigation.
A source can run any combination of detectors, and you can build your own when the built-in ones don’t cover what you’re looking for.
Findings: what a detector produces
Every time a detector spots something, it raises a finding — one flagged match, on one asset. A finding carries:
- What matched and the surrounding text, so you can judge it in context without digging through the original document.
- Severity — how serious it would be if it’s a real issue, from Critical down to Info.
- Confidence — how sure the detector is that the match is real, separate from how serious it would be.
A high-severity, low-confidence finding deserves a quick look. A high-severity, high-confidence one deserves action. Findings persist across scans rather than piling up as duplicates — the same issue is tracked over time, and your decisions (marking something resolved, ignored, or a false positive) are always respected by later scans.
Where this leads
A flat list of findings is a start, not an answer. The next step is working out which findings actually matter — that’s what Ranking & the Semantic Layer does.
For the full technical reference, see Sources, Detectors, and Findings & Results.