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A modular, open-source platform that enables small businesses to anonymously share and compare KPIs across industries and regions, without exposing raw data. It uses policy-driven data exchanges with data contracts and secure aggregation, offering of
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README.md

OpenBench: Privacy-Preserving Benchmarking (MVP)

  • This repository contains a minimal, working MVP of the OpenBench platform focused on:
  • An offline-first KPI data model (Revenue, COGS, inventory turns, lead time, CAC, LTV).
  • A simple, privacy-preserving aggregation primitive (Laplace-noise-enabled) for anonymized benchmarking.
  • A lightweight Python packaging setup compatible with pytest-based tests and python -m build packaging checks.
  • A minimal data-contract system (DataContract) and an in-memory contract registry for versioned payload schemas.
  • A pluggable adapter framework with sample POSAdapter and ERPAdapter to bootstrap data ingestion.

How to run

  • Install dependencies and run tests: bash test.sh
  • The MVP stores KPI records locally in a JSONL file under the package data directory.

Project layout (high-level)

  • openbench_privacy_preserving_benchmarkin/core.py: Core data model and analytics primitives.
  • init.py: Re-exports core primitives for simple imports.
  • test.sh: Quick test runner that also builds the distribution.
  • AGENTS.md: Swarm agent guidelines describing architecture and testing commands.
  • pyproject.toml/setup.py: Packaging metadata to satisfy python packaging checks.

This is a deliberately minimal MVP intended to demonstrate architecture and testing practices.