Turn raw fraud flags into actionable intelligence.
PyGuard is an AI fraud analyst that reads your flagged transaction table, writes an executive-ready report, and keeps answering follow-up questions inside Slack. It does not replace your team. It takes the mechanical review work off their plate so the decisions still belong to humans.
- Runs inside
- Slack
- Output
- PDF reports
- Human review
- Always
February 2025 fraud intelligence report attached below. 2,189 transactions processed. 788 flagged (36.0%). Two attack waves identified on Feb 10-14 and Feb 18-20. Full report follows as PDF.
Ninety seconds inside a real Slack workspace
A short walk through of the live product, from a flagged transaction table to an in-thread report, a follow-up question, and a LaTeX edit. No slides and no mockups.
A short, 1-3 minute promotional video showing the live PyGuard product in action: a Slack user asks for a fraud intelligence report, PyGuard posts a PDF into the thread, and then fields a follow-up question and a LaTeX edit inside that same thread. No slides and no mockups.
PyGuard in sixty to ninety seconds
Live product flow inside a real Slack workspace.
End-to-end walkthrough
Installation, first report, follow-up Q&A, and LaTeX edits. Designed so a new user can reach the primary workflow without reading anything else.
Follow the workflow yourself
Pairs with the setup guide. If you watch this once, you can run the end-to-end workflow on your own Slack workspace without referring back to the docs.
An end-to-end walkthrough covering installation, first report generation, in-thread Q&A, and LaTeX edits. Designed so a first-time user can follow along and reach the primary workflow without reading any other documentation.
Flag fatigue is the real bottleneck for SMEs
Most teams already run some form of fraud detection. The hard part is never the detection itself, it is what happens after the model finishes and hands you a long table of flagged transactions.
Flagged rows per month on a typical SME fraud feed.
Very few SMEs can justify a full-time fraud analyst or data scientist.
Spent every week pivoting spreadsheets and writing summary emails.
Executives and operations leads end up as accidental fraud analysts. They spend the afternoon inside a spreadsheet instead of working on the business. We built PyGuard because we wanted to soften that specific manual step, not remove the human from the loop.
The novelty is not the analysis. It is the action.
Plenty of tools can describe fraud. What very few of them do is translate that description into prioritised, specific actions a small team can hand straight to operations, engineering, or risk.
Narrative over spreadsheets
PyGuard writes the report the way a human analyst would: named patterns, business framing, clear numbers, and the parts of the month that actually changed.
Actions you can run tomorrow
Every report ends with prioritised recommendations, split into IMMEDIATE, SHORT-TERM, and MEDIUM-TERM. Each one is specific enough to assign.
Answers inside the thread
Once the PDF is in Slack, PyGuard stays in the thread. Ask a follow-up, request a rewrite, or tighten the executive summary without leaving the conversation.
From CSV to decision in four steps
The goal is to keep the conversation in one place. You never have to leave Slack, and you never have to open a spreadsheet to understand what PyGuard did.
Raw transaction table
Ask in Slack
PDF lands in thread
Review, ask, edit
A thin agent layer on top of your existing detection
PyGuard is not a detection engine. It sits above your model, reads the flagged data, and orchestrates a small set of sub-agents to produce a report, answer questions, and apply edits in place.
flowchart LR User["SME operator in Slack"] --> Slack["Slack Socket Mode"] Slack --> API["PyGuard backend"] API --> Memory["Memory agent"] Memory --> Fraud["Fraud orchestrator"] Fraud --> Data["Fraud data agent"] Fraud --> Pattern["Pattern agent"] Fraud --> Report["Report agent"] Report --> PDF["PDF report in thread"] PDF --> User
We reduce manual review. We do not remove the human.
PyGuard is built to make an accidental fraud analyst faster at the mechanical parts of their job. Every decision still belongs to a person, and every report still gets reviewed.
- Reports are a starting point for human review, not a final decision.
- Every actionable recommendation is owned by someone on your team before it ships.
- Edits are explicit requests. PyGuard never rewrites a report on its own.
- Every previous version is saved locally so a rollback is one step.
“PyGuard is not trying to replace a fraud analyst. It is trying to take the most tedious parts of their job and hand them a better starting point.”
“Detection is already solved for most SMEs. What is not solved is turning the detection output into a decision a small team can act on this week.”
Three doors in
Install and run PyGuard locally in under thirty minutes, dive into the docs, or help us shape the beta.
Setup guide
Prerequisites, backend install, Slack app setup, and your first report. Each step has expected output and a failure hint.
Documentation
Features, troubleshooting, and the API reference. Full Wiki, rendered in-site.
Contribute
File a bug, suggest a feature, or read the triage SLA. Links straight to our GitHub issue templates.
Prefer to read the repo first? Browse PyGuard on GitHub.