Peritiq provides a cloud‑based platform that gathers blind, anonymous employee feedback and cultural diagnostics, then combines this data with strategic objectives to generate data‑driven recommendations. The EU‑hosted, GDPR‑first solution includes real‑time analytics and works‑council compliance features, helping European enterprises align team dynamics with business goals.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations often struggle to align team dynamics, cultural factors, and strategic objectives, leading to decisions that overlook employee perspectives and increase bias. Traditional decision‑making tools lack built-in mechanisms for blind, anonymous input and may not comply with EU data‑privacy regulations or works‑council requirements.
Solution
Peritiq offers a cloud‑based decision‑making platform that captures and analyzes team input, cultural insights, and strategic data in a single layer. The system collects blind, anonymous feedback to minimize social bias and aggregates responses into data‑driven recommendations that link cultural health to strategic outcomes. Hosting is EU‑based and GDPR‑first, ensuring compliance with European privacy laws and supporting works‑council governance. By visualizing cultural metrics alongside strategic priorities, the platform helps leaders identify gaps, prioritize initiatives, and track progress over time.
Target Audience
Primary users are mid‑size to large enterprises in Europe that need to incorporate employee input into strategic planning while meeting data‑privacy and works‑council requirements.
Features
- Blind and anonymous survey engine that removes respondent identity to reduce bias
- Integrated cultural diagnostics that map employee sentiment to strategic goals
- GDPR‑first, EU‑hosted architecture with built‑in works‑council compliance features
- Real‑time analytics dashboard delivering data‑driven recommendations and trend visualizations
- Decision‑layer tools that combine team dynamics, cultural data, and strategic planning in one workflow