QAWacht

AI-powered Quality Assurance for Human Teams and AI Agents

Monitor and evaluate voice and text interactions — recorded or in real time.

Real-time detection of critical issues and risks

Voice and text, recorded files and real-time streams

Germany-based data hosting and operations

SaaS, Pay as you go

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QAWacht is an AI-powered quality assurance platform for monitoring and improving both human teams and AI agents. It analyzes voice and text interactions, works with recorded data or real-time streams, and helps detect quality issues, risks and deviations as they occur.

For human-agent environments, QAWacht works independently of the existing contact center software through a local connector. AI-agent monitoring can be integrated through APIs or dedicated connectors.

Quality Monitoring and Control

Human teams and AI agents / bots
Voice and text
Recorded files and real-time streams
Up to 100% of interactions
Multi-language
Fully customizable criteria in natural language
Scoresheets and transcripts

QAWacht continuously evaluates interactions across human and AI-operated channels, making quality control scalable, consistent and immediate.

Quality Assurance for AI Agents

Monitor individual AI agents or entire agent teams
Detect incorrect responses and process deviations
Identify recurring weaknesses and failure patterns
Use QA results to tune prompts, templates and agent configuration
Compare quality across human and AI teams

Evaluation results, transcripts and detected patterns can be used to refine prompts, templates, agent logic and other configuration parameters.

Real-time Monitoring and Assistance

Real-time detection of critical issues, faults and risks
Situation-specific guidance for human agents
Live monitoring of human and AI-agent interactions
Targeted training based on live and recorded interactions

Reviews and Continuous Improvement

Dashboard and reports for QA managers
Flexible score ranges in metrics’ criteria
Default metrics criteria available
Manual verification and correction based on transcripts
Drill-down from trends to individual interactions
Identification of recurring weaknesses in human or AI agents
The final decision always remains with the authorized QA manager

QAWacht supports quality decisions but does not replace human judgment. AI-generated evaluations can be reviewed, corrected or overridden, and the final decision remains with the responsible QA manager.

Reports and Dashboards

Detailed reports
Filters by human or AI agent, team, metric and assessment parameter
Dashboard with consolidated data and trends
Detailed interaction-level analysis
Exporting detailed data to XLS/CSVV

QA managers can set filters in quality reports to identify targets for manual verification. This makes it possible to detect both recurring quality patterns and individual human or AI agents whose performance deviates from the expected standard.

Independence

Contact-center-software-independent monitoring for human agents
Local connector — no server-side integration required for human-agent environments
API or dedicated connector integration for AI agents
Live quality monitoring without mandatory voice recording recording
Works with both real-time streams and recordings
Supports softphone and web-phone environments

Flexibility and modularity

SaaS, private cloud or on-premise
Interchangeable LLMs
English / German / Spanish preinstalled for admin interface and analysis
Multi-language operation
Designed as part of the SoftBCom Platform
Can work together with telephony, ticketing and AI agents within end-to-end process execution
Future-proof architecture

We continuously evolve the platform as new technologies become available, keeping QAWacht adaptable and future-proof.

Data Sovereignty and Privacy

Designed for privacy-sensitive environments
Germany-based hosting and operations
Data minimization, fragmentation and anonymization
Local LLM deployment available

Video #1: Watch how to get started with QAWacht in under 15 minutes

This training video explains how to use the QAWacht connector for quality monitoring of voice communications in a contact center.

Video #2: Quick Start. How to Organize AI-Based Quality Monitoring

In this video, we will show how to organize continuous quality monitoring of contact center conversations using AI.
QAWacht automatically analyzes voice and text interactions, generates transcripts, evaluates quality, and helps quickly identify problematic interactions across human and AI-operated channels.
We will walk through the basic process of setting up the system and performing the first test evaluations.

1 token = 1 QA minute (LLM input / output cost included)

250 tokens for connecting 1 active agent per month

Match and mix:

analyze recorded voice or text interactions

monitor live human or AI-agent interactions in real time

or combine both in one QA project

Launch QA right away with audio files

Launch project in under 15 mins with live agents

Match and mix: flexible setup — use audio file uploads, real-time QA for agents, or a mix of both

Flexible setup — use recorded files, real-time QA, or a combination of both

Free trial

Get started with QA Wacht for free. Experiment with metrics and reports, save money on quality assurance management from day 1.

Up to 14 days

Full functionality

400 tokens included, free of charge

No credit card needed

Seamless transition to paid project

See QAWacht in action

Business

Start your quality assurance project now!

Pay as you go

Token packs start at $45 (View prices)

The bigger the package, the cheaper the tokens

Cancel anytime

Schedule a Free Trial Demo

Enterprise

Special conditions for large organizations

Dedicated support

Custom pricing

On-premise / private cloud possible

Business	Pack 1	Pack 2	Pack 3	Pack 4
Tokens	1 500	6 000	25 000	100 000
Pack price, $	45	165	625	2 000
Per token, $	0,030	0,0275	0,025	0,020