Enterprise Anomaly Detection Platform (EDAP)
An architecture for collecting events across business systems, detecting anomalies, scoring risk and recommending controlled actions.
Overview
EDAP is an enterprise data and anomaly detection concept. It collects business events from ERP, databases, APIs and IoT sources, normalises them, detects anomalies and turns them into scored, actionable alerts.
Problem
Irregularities such as unusual sales-order patterns are spread across many systems and are usually noticed late, if at all, because no single system sees the whole picture.
Solution
Collect events from each source into a common shape, run detection over the normalised stream, score the risk of each finding, alert the right people and recommend a next step, with any automated action kept under control.
Architecture
- 01
Sources
- SQL Server
- PostgreSQL
- ERP
- APIs
- Google Workspace
- IoT
- 02
Collection
- Event outbox
- Change data capture
- 03
Normalization
- Common event schema
- 04
Detection
- Rules
- AI / ML models
- 05
Risk scoring
- Business-impact score
- 06
Response
- Alert
- Recommendation
- Controlled action
- Business events (for example "sales order approved") are modelled separately from raw database changes.
- An ERP event outbox combined with SQL Server change data capture is the intended reliable event source, with idempotency and replay.
- Sales-order header and row data is a first target for auditing scenarios.
Key features
- Multi-source event collection
- Normalised event model across systems
- Anomaly detection and risk scoring
- Alerts with recommended follow-up
Engineering challenges
- Distinguishing meaningful business events from low-level row changes.
- Reliable event delivery: idempotent consumers, replay and observability.
- Keeping automated actions safe and reviewable.
Results
No measured outcomes are published for this project. I only list results that have been verified.