Product

Audiem

Audiem is an AI-powered workplace experience and employee feedback analytics platform. Founded in 2022 and headquartered in London, the company specializes in using advanced, transparent text analysis to process unstructured written comments into actionable HR, IT, and operational insights.

Analytics & Data Workplace Advantage Ltd

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About Audiem

Audiem is a specialized enterprise SaaS and AI-driven data analytics provider founded in 2022 in London, UK. The platform translates vast quantities of unstructured, qualitative text—such as open-text employee survey responses, internal helpdesk communications, and chat logs—into structured, actionable operational insights. By transforming highly subjective human language into precise quantitative metrics, Audiem enables Human Resources, IT operations, and corporate real estate executives to optimize the modern corporate work environment. 🧠 The Academic Foundation: The Workplace Mix Unlike generic Large Language Models (LLMs) that lack context-specific awareness, Audiem relies on its proprietary, expert-trained academic framework called the Workplace Mix. Developed alongside workplace scientists and organizational psychotherapists, this classification engine maps unstructured comments against distinct physical, digital, and social pillars: • Physical Infrastructure: Pinpoints employee sentiment regarding ergonomics, office layout efficiency, acoustic management, and localized thermal comfort. • Digital & IT Ecosystem: Surfaces hidden software friction, connectivity issues, hardware deficiencies, and application adoption hurdles. • Cultural & Social Dynamic: Examines collaboration velocity, psychological safety, managerial behavior, and systemic administrative friction. ⚙️ Technical Capabilities & Data Engineering • Multi-Channel Data Ingestion: Aggregates qualitative feedback via automated APIs, legacy XLS/CSV uploads, Zendesk or ServiceNow helpdesk tickets, and on-site physical QR codes. • High-Accuracy Text Mining: Evaluates written communication in 100+ native languages with a validated 95%+ precision rate, preventing the mischaracterization of localized slang or corporate terminology. • Semantic Relationship Mapping: Looks beyond simple keyword matching to uncover hidden conceptual connections (e.g., automatically identifying that complaints about "concentration" are mathematically linked to "open-plan seating" acoustics).

Key Features

Automated Feedback Aggregation: Ingests unstructured member data seamlessly from digital helpdesks, member portal chats,Thematic "Workplace Mix" Classification: Uses an expert-trained framework to group open-text member comments into specifSemantic Relationship Mapping: Links hidden concepts automatically to show exactly why a problem occurs• Predictive Churn Detection: Flags declining member sentiment and recurring complaints early so operators can interveneMulti-Location Heatmaps: Visualizes sentiment trends across different coworking sites, floors, or hot-desking zones on a• Multi-Language Analysis: Processes written feedback in 100+ native languages with over 95% accuracy to support global,

Frequently Asked Questions

How does Audiem capture feedback from casual coworking members without causing survey fatigue?

Audiem bypasses long, boring surveys by embedding quick, open-text feedback prompts directly into daily touchpoints like Wi-Fi login landing pages, member portal chat widgets, and physical QR codes placed around hot-desking zones and meeting rooms.

Can Audiem tell the difference between a one-off complaint and a systemic issue across multiple workspace locations?

Yes, Audiem’s Semantic Relationship Mapping groups localized complaints into broader themes, allowing operators with multiple sites to instantly see if a complaint about "bad Wi-Fi" or "poor acoustics" is isolated to a single phone booth or is a recurring issue across their entire global portfolio.

How does the platform help us actually prevent member churn and increase desk retention?

Instead of relying on trailing data like exit interviews, Audiem's AI tracks real-time sentiment drops in daily helpdesk tickets and community chat channels, automatically flagging clusters of frustration—such as broken printers or overcrowded breakout spaces—so community managers can fix the problem before a member decides to cancel their subscription.