Every leakleaves a trail.
Classifyre is an open-source investigation platform for your data estate. It scans the systems you already run, detects secrets, PII, and the signals you define — then works them like a detective: standing inquiries, fingerprints, cases, competing hypotheses, and an AI autopilot that does the legwork between scans.
Lead investigator
On duty since your last scan
Source types
30+
Databases, lakehouses, collaboration tools, BI, and web content.
Detector families
6
Built-in packs for PII, secrets, and security — plus four custom engines from regex to any LLM.
AI decisions explained
100%
Every autopilot action lands in the audit trail with a written rationale.
Follow the evidence
One pipeline runs from the systems you connect to a resolved investigation: sources become assets, detectors raise findings, findings feed inquiries and fingerprints, and everything converges into cases. Here is one real night in the life of it — a credential leaking through CI logs, traced end to end.
A scheduled scan walks the bucket
Your CI pipelines ship their logs to an S3 bucket, and Classifyre scans it on a schedule — no agents, no data migration. Every log object becomes an asset in the catalog, carrying its metadata: path, size, content type, when it last changed.
The secrets pack recognizes a key
Detectors read each asset as it lands. The built-in secrets pack matches an AWS access key pasted into a deploy job's log and raises a critical finding — with the exact match, its location, and a deterministic identity so a re-scan updates it instead of duplicating it.
A question you already asked gets its answer
Months ago someone phrased a standing question: “Are credentials leaking through CI logs?” The new finding matches inquiry #7 automatically. No new alert channel, no duplicate monitor — the question you already asked just accumulated evidence.
The same key surfaces somewhere else
The finding's fingerprint matches a record from a quarterly S3 export scanned last month. Two systems, one leak — connected by identity, not by someone eyeballing two spreadsheets at 2 a.m.
Case #42 opens with two explanations
“Credential exposure” opens as a case with both findings attached as evidence and two competing hypotheses: the key leaked via CI logs, or it lingers in a stale export. Each hypothesis is pinned to the evidence that supports or contradicts it — confirm one, kill the other, resolve the case.
Nobody was at the keyboard
Steps three through five happened while you slept. Harness AI matched the inquiry, linked the fingerprint, opened the case, and drafted both hypotheses right after the scan — and logged a written rationale for every single move. You arrive in the morning to a case, not a pile of alerts.
Findings are evidence.
Cases are the product.
Most scanners stop at a findings table and wish you luck. Classifyre treats every finding as evidence in an ongoing investigation — connected to the questions you are asking, the cases you are working, and the explanations you are testing.
Standing questions that keep watching
Phrase what you actually want to know — “Are credentials leaking through CI logs?” — and the inquiry keeps matching new topics and findings against it, scan after scan.
Evidence with an owner and a lifecycle
Findings get attached to cases instead of dying in a CSV export. Each case carries its evidence, status, and history toward an actual resolution.
Competing explanations, pinned to evidence
Work a case like an analyst: propose explanations, link each one to the findings that support or contradict it, and watch the graph confirm or kill it.
Humans and AI in one audit trail
Teammates and the autopilot operate on the same cases, with every action — human or AI — attributed and explained in a shared record.
Autopilot, not copilot
A copilot waits for you to type a prompt. Harness AI doesn't wait. After every scan, five specialized agents wake in sequence, read a system brief composed from live facts and long-lived memory, and move the investigation forward on their own — deduping findings, building cases, tuning silent sources, even authoring the detectors you were missing. The fifth agent literally dreams: it consolidates what the others learned while nothing else is running.
- 01InquiryKeeps standing questions answered
Matches fresh findings to your inquiries and dedupes the rest — similar signals collapse into one monitor instead of a flood.
findings.searchinquiries.enrich - 02CaseBuilds the investigation
Opens and enriches cases: drafts competing hypotheses, attaches evidence, and links findings into the case graph.
cases.createcases.add_hypothesis - 03ConfigWakes up silent sources
Profiles sources that ingest data but produce nothing, then enables the detectors that fit the data shape — no manual setup.
assets.profileconfig.tune_source - 04Detector AuthorWrites the detector you were missing
When findings slip through, it hypothesizes a detector, dry-runs it, ships it, and verifies the results on the next cycle.
detector.testdetector.create - 05DreamConsolidates what it learned
Curates long-lived memory and refreshes the system brief so every agent starts the next cycle grounded in today's reality.
memory.rewritesystem_brief.update
A flight recorder, not a black box
Each agent runs a resumable reason → act loop: it reads the live system brief, calls real tools, and writes back what it did and why. Watch one cycle play out — it's the same credential-leak night from the story above, every decision audited, every deliberate non-action recorded too.
- Grounded in facts. The system brief is composed by the server every cycle — coverage, glossary, topics, gaps — from live counts plus learned memory. Only the short overview is model-written.
- Idempotent & resumable. Runs persist mid-loop and resume without replaying work, so side effects never double-fire.
- You stay in command. Steer it with a one-line instruction, or flip observe-only and it proposes without touching a thing.
A memory you can read
Harness keeps a long-lived memory of your instance — business glossary, decision precedents, topic-to-inquiry maps. Every cycle, the server composes it into a system brief: live counts and learned facts in fixed sections, with only the short overview written by the model. Inspect and edit any of it.
No findings? It makes some
Connect a source with no detectors and there is nothing to react to — so Harness profiles the ingested assets instead: column names, mime types, field shapes. From that metadata alone it hypothesizes a detector, dry-runs it against samples, ships it, and checks the results on the next cycle.
Observe-only when you want it
Every action — and every deliberate non-action — lands in one audit trail with a written rationale, attributed to the agent that made it. Flip the whole instance, or a single case, into observe-only and Harness proposes without touching a thing.
When you do want to talk, the assistant drives setup
The autopilot runs your investigations without being prompted. For everything else there's the assistant: it narrows scope, stages source and detector configuration, and hands back an exact operating plan instead of leaving you in a generic chat loop.
Classifyre Assistant
Example walkthrough
Scan the systems you already own
Classifyre is built for mixed estates: operational databases, lakehouse and warehouse platforms, collaboration systems, analytics assets, and public-facing content — all feeding evidence into the same investigation layer.
Databases
Operational and document stores for row and collection scans.
Graph Databases
Graph-native stores with node and relationship traversal.
Warehouse & Lakehouse
Analytical compute platforms and catalog-first ingestion.
Streaming
Event streams and message brokers sampled for content.
Web & UGC
Public-facing websites and user-generated content.
Social Media
Social and video platforms with public posts and transcripts.
Collaboration
Team communication and workspace activity streams.
Analytics & BI
Dashboards, reports, and business intelligence assets.
Evidence on day one
Switch on curated built-in packs — PII, secrets, security, moderation, quality — and findings start flowing into your investigations immediately. No model wrangling required.
Secrets & Credentials
Credential leaks, code issues, and high-risk security signals.
Privacy & PII
Personal data detection, OCR privacy, and de-identification checks.
Threats & Attacks
Active threat indicators such as prompt injection attacks.
Content Quality
Spam, duplicates, plagiarism, readability, and language quality signals.
Classification & Tagging
Domain, content type, sensitivity tiers, and jurisdiction tagging.
From a regex to any model
Custom detection is a ladder, not a leap. Start with a deterministic rule, climb to zero-shot text understanding, plug in open transformer models for text and images, and top out with an LLM detector for the signals nothing else can catch. Every rung feeds the same findings stream.
Regex & rules
Deterministic pattern matching for IDs, secrets formats, policy phrases, and internal codes. Instant, explainable, zero ML overhead.
Entities & classification
Extract entities and classify text in a single model pass, using labels written in your own words. Contextual understanding without training a model.
Any Hugging Face model
Plug in open models for text classification, image classification, object detection, and embeddings. Yes — Classifyre sees images, not just text.
Bring any LLM
Write a prompt, define labels and extraction fields, and any configured LLM provider becomes a detector — for signals too fuzzy to define any other way.
On your desk tonight.
In your cluster later.
The same open-source platform runs at three altitudes: a desktop app for one investigator, a Helm chart for a team, and an enterprise partnership when it becomes company infrastructure. Nothing you build at one altitude is thrown away at the next.
Download the app
Classifyre Desktop is the complete platform in a single install — the same product that runs in production, with PostgreSQL embedded and every scan worker running in its own isolated sandbox under the hood. Not a demo, not a trial: it's how a single investigator runs Classifyre day to day. Everything stays on your machine.
Free · Open source · No signup, no cluster, no sales call
Helm on Kubernetes
Deploy the same open-source core to Kubernetes — self-hosted or in your cloud — with properly separated components and ephemeral processing workers that scale to zero between scans. Your infrastructure, your data.
helm install classifyre \
oci://registry-1.docker.io/classifyre/classifyre-core \
--version 0.4.50A partnership,
not a license key
The enterprise layer adds what a regulated, global rollout needs — and it comes with us attached. Our engineers work with your team from the first pilot: we learn how your business names things, tune detection to your language, and tailor Classifyre to the way your company actually works.
The org chart, wired in
Authentication, authorization, roles, and governance — the layer the open-source core deliberately leaves out, built for regulated rollouts.
Detection that speaks your language
Models tuned on your terminology and document shapes, so “account number” means what it means at your company — not on the internet.
Built for your domain
Detectors, sources, and multilanguage support engineered around the data your industry actually produces — with our engineers doing the building.
We stay in the room
From first pilot to global deployment: architecture reviews, upgrade assistance across Kubernetes and OpenShift, and SLA-backed support.
Open your first case tonight.
Download the desktop app, point it at one system you already run, and see what the investigator finds. Everything stays on your machine — and everything you build carries over when you scale.