# Observe.AI Auto QA (Observe.AI)

Category: AI quality assurance. Public reviews about the AI: 19. Profile updated 2026-10-08.

Contact center QA that fills in evaluation forms automatically using AI scoring and rules.

## What it does

Observe.AI's Auto QA analyzes customer-agent interactions and fills in QA evaluations using AI, rule definitions, calibration, metadata and context [1]. A drag-and-drop rule builder lets teams define key moments with keywords, phrases and logic [1]. Agents can acknowledge or dispute evaluations [1]. Observe.AI's Performance Agents use behaviors defined in AutoQA to build coaching sessions that supervisors review and approve [2].

Channels: Voice, Chat. Works with: Observe.AI's integrations directory names telephony and contact center platforms including 8x8, Aircall, Amazon Connect, Avaya and CallRail [3].

## Controls

- Evaluation rules are built in a drag-and-drop rule builder using keywords, phrases and logic [1]
- Calibration features auto-suggest, auto-fill and auto-submit evaluations [1]
- Agents can acknowledge an evaluation or dispute it for supervisor feedback [1]
- Supervisors review, adjust and approve each AI-built coaching session [2]

## Setup

Not published; Observe.AI offers setup through a demo request [1].

## Billing

By contract. Not published; sold by contract through a demo request [1].

## Vendor claims (not results)

- Observe.AI says Auto QA assesses 100% of customer interactions [1]
- Observe.AI states a 23% reduction in average handle time, a 10% uplift in conversions, a 13% increase in revenue and a 97% improvement in compliance monitoring [1]
- Observe.AI says manual QA evaluations are completed five times faster [1]

## Not found in public sources

- Public prices
- Whether a reviewer can override a single AI-scored answer, beyond agent disputes
- Email support for Auto QA
- Help desk or ticketing integrations
- How Observe.AI measured the handle-time, conversion and revenue figures

## What reviewers say

- Does the job: 89% positive (17 of 19; 95% interval 69%–97%)
- Accuracy: 0% positive (0 of 11; 95% interval 0%–26%)
- Handoff to a person: 0 positive, 0 negative (too few to rate)
- Cost: 1 positive, 2 negative (too few to rate)
- Setup and upkeep: 62% positive (8 of 13; 95% interval 36%–82%)

> "The AI-powered conversation analysis reduces the need to manually review calls, making it much easier to identify trends, coaching opportunities and areas for improvement." (Callum R., G2 (via AWS Marketplace), 2026-09-30: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "I've also found that AI-generated insights aren't always perfect, especially when a conversation has a lot of context, industry-specific terminology, or an unusual customer query." (Izzy H., G2 (via AWS Marketplace), 2026-09-15: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "It helps me reduce the time needed for manual reviews and makes it easier to understand customer feedback." (Abhishek S., G2 (via AWS Marketplace), 2026-08-28: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "Its call analysis and quality monitoring features help me spot trends, highlight coaching opportunities, and improve agent performance without requiring extensive manual review." (Tariq, G2 (via AWS Marketplace), 2026-08-27: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "Transcription, sentiment detection, and identifying specific moments in conversations aren't always accurate, so the results sometimes need manual verification." (Subhashree S., G2 (via AWS Marketplace), 2026-08-20: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "It struggles with word detection when there are heavy accents, background noise, or overlapping speech, which then leads to issues with downstream evaluation." (Shaunak J., G2 (via AWS Marketplace), 2026-08-14: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "Certain features may require additional training and customization, and occasional inaccuracies in AI-generated insights can require manual review and validation." (Ayush A., G2 (via AWS Marketplace), 2026-08-14: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "The 100% analysis eliminates the blind spots of manual evaluations based on simple samples." (Geraud S., G2 (via AWS Marketplace), 2026-08-12: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "I like Observe.AI for its standout ability to analyze 100% of customer interactions through automated quality assurance. This feature leads to a massive time reduction" (Ranganath D., G2 (via AWS Marketplace), 2026-08-09: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "The accurate transcription and analysis have made our QA process much more efficient, allowing quick review of transcripts and focusing on key interactions." (Krishan Y., G2 (via AWS Marketplace), 2026-08-04: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "I like how it evaluates my customer interactions and reduces the time spent on manual quality assurances, which helps with a lot of things." (Matshediso R., G2 (via AWS Marketplace), 2026-07-29: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "The initial auto-scoring and workflow configuration often require significant tuning to avoid false compliance flags, especially when dealing with custom industry jargon or thick accents." (Mitali V., G2 (via AWS Marketplace), 2026-07-23: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "the AI is not intelligent enough to determine tone correctly at a high enough percentage to use that in our AQA" (Entertainment, G2 (via AWS Marketplace), 2026-07-23: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "It eliminates the manual effort of reviewing calls, ensures consistent quality evaluations, and helps improve agent performance." (Mohit S., G2 (via AWS Marketplace), 2026-07-21: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "It grades our calls for us instead of manually listening to each call. It gives us a better picture about a CSR performance if a problem is a one-off or is a pattern." (Telecommunications, G2 (via AWS Marketplace), 2026-07-17: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "sometimes it marks errors that are not due to following very random patterns" (Guadalupe X., G2 (via AWS Marketplace), 2026-07-04: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "The evaluation of our interactions is made efficient and professional through a consistent and automated approach" (Ifeoma E., G2 (via AWS Marketplace), 2025-10-20: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "It regularly underperformed compared to a human QA team paired with a small in-house script written by engineering plus or minus some UI functionality." (James G., G2 (via AWS Marketplace), 2025-08-09: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)
> "We've seen instances where the system flags false positives, often due to words that sound similar to profanity but are actually appropriate in context." (Sammy P., G2 (via AWS Marketplace), 2025-07-23: https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-lgvu5s7gueesi)

## Sources

1. [Auto QA for Contact Centers](https://www.observe.ai/post-interaction/auto-qa), Observe.AI, accessed 2026-10-08
2. [Performance Agents](https://www.observe.ai/ai-agents/for-operations/performance-agent), Observe.AI, accessed 2026-10-08
3. [Integrations](https://www.observe.ai/platform/integrations), Observe.AI, accessed 2026-10-08

Canonical page: https://get-support-ai.com/products/observe-ai