Sold by: Instadesk
Deployed on AWS
Leveraging cutting-edge Generative AI and voiceprint recognition, Instadesk AI Quality Inspection automates the monitoring of customer service interactions across voice, text, and email. It reduces complaint risks by up to 65% and labor costs by 30%, ensuring global compliance and delivering deep insights into agent performance and customer sentiment.
Overview
As overseas enterprises scale their customer service operations and increasingly deploy AI Agents, traditional manual quality inspections are proving insufficient for risk management. According to recent mandates, industries urgently need "AI + Quality" integration to enhance control accuracy. However, the inherent "hallucinations" and non-deterministic outputs of LLMs present new compliance challenges for contact centers.
Instadesk AI Quality Inspection is an enterprise-grade solution specifically designed for global business. It overcomes the low coverage and subjectivity of traditional methods while filling the gap in evaluating AI Agent behavior.
LLM-Powered Precision: Utilizing proprietary vertical LLM and voiceprint recognition, it goes beyond keywords to understand context, sentiment, and intent. For AI Agent interactions, it evaluates coherence and accuracy, achieving up to 93% recognition accuracy.
Omni-channel Compliance & Risk Interception: Integrates seamlessly with call centers, live chat, email, and major social media channels like WhatsApp. The system monitors compliance in real-time, alerting on high-risk sentiments (e.g., escalation tendencies) with a 90% violation interception rate.
Data-Driven Efficiency: Moves beyond random sampling. Automated multi-dimensional reports pinpoint service gaps. Proven to increase first-time resolution rates by 40% and sales execution efficiency by 90% while significantly reducing manual review costs.
Highlights
- Risk & Compliance Lifeline: Full-Volume LLM Monitoring Audit 100% of interactions (Voice & Text) instead of the traditional 1%-5% sampling. Accurately identifies compliance red lines in sensitive industries like finance and collections using semantic understanding, reducing complaint risks by up to 65%.
- The Ultimate Evaluator for AI Agents One of the first solutions to support quality evaluation of GenAI Agents. It assesses not just script adherence, but also tool-calling accuracy, logical reasoning, and human-likeness, ensuring your automated assistants don't fail in production.
- Operational Revolution: 40% Increase in FCR Features an Intelligent Sales Assistant that provides real-time prompts on standard procedures and scripts, increasing execution efficiency by 90%. Combined with sentiment analysis, it shortens handling times and empowers human agents to focus on high-value tasks.
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
QA_product | platform_fee_for_QA | $20,000.00 |
VoiceQA_10k_hours | 10k_hours_voice_pack | $12,500.00 |
Text_seat | text_analysis_per_seat_pricing | $250.00 |
LLM_token | per_100M_tokens_usage | $1,000.00 |
Dimensions summary
This contract combines a base platform fee with usage-based add-ons you scale to your workload. The QA_product dimension covers the platform fee to access AI Quality Inspection. On top of that, you add capacity as needed: VoiceQA_10k_hours bundles voice analysis in 10,000-hour packs, Text_seat charges text analysis per seat, and LLM_token bills large language model usage per 100 million tokens. You mix these dimensions to match how you use voice, text, and AI processing. Voice, seats, and tokens each scale independently, so you buy more units only where your volume grows.
Top-of-mind questions for buyers
A seat maps to one user account that runs text analysis on chat, email, and message channels. You buy a seat for each person who needs text inspection access. Seats bill independently from voice hours and token usage, so you add seats only as your text team grows.
VoiceQA_10k_hours bundles voice inspection in blocks of 10,000 hours. The system meters the audio time it analyzes against that pack. When you need more voice capacity, you add another 10,000-hour pack. Voice packs scale separately from seats and tokens.
You always pay the QA_product platform fee to access AI Quality Inspection. On top of that, three usage dimensions bill independently and add together: voice packs by 10,000-hour blocks, text by seat, and LLM_token per 100 million tokens. Voice volume tends to dominate for heavy call-center use.
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