Klarna2024
Replaced 700 agents. Now rehiring.
CEO publicly admitted 'lower quality'. Valuation: $45.6B → $6.7B.
Would have fired
cheerful_abandonmentResolution-path collapse across 12K post-replacement tickets.
Your QA scores look great. Your customers are leaving anyway.
QA tools check grammar and greetings. Meanwhile, your bot just said "You got it!" to a customer who rage-quit. That conversation scored 94%.
100 conversations free · No credit card required
Integrates with your existing stack
Zendesk
Intercom
Gorgias
Salesforce
HubSpot
Freshdesk
Gmail
WhatsApp
Claude
Zendesk
Intercom
Gorgias
Salesforce
HubSpot
Freshdesk
Gmail
WhatsApp
Claude
Zendesk
Intercom
Gorgias
Salesforce
HubSpot
Freshdesk
Gmail
WhatsApp
Claude
The blind spot
Legacy QA scores this 92%. AINGEL scores it 0.75.
A real customer conversation, scored in real time. Greeting present, grammar fine, tone cheerful — and the customer just churned. Here's what AINGEL reads between the lines.
Live · Case #A-4471
Intercom · Bot "Iris"AINGEL · EQ Analysis
—.—
out of4.00
Critical failure
Revenue at risk
$—· est. CLV lost
Likely churn · Brand damage
Read
Conversation read in real time
Every turn, every turn after.
Evaluate
19 business-impact flags evaluated
Not generic sentiment. Specific failure modes.
Quantify
Revenue at risk quantified
In dollars. Using your CLV, your industry.
0%
Leave over indifference
Bain & Company
1 in 26
Actually complain
TARP Worldwide
25-95%
Profit increase from retention
Harvard Business Review
<0min
Setup time
How it works
Three steps. Five minutes.
5,426 conversations synced
Methodology
Not a scorecard. A diagnostic.
Competitors ship a blank canvas and ask you to define quality. AINGEL ships a three-layer diagnostic calibrated against CXBench — a multi-industry labelled corpus. Every conversation reads through all three layers, every time.
Layer01
Technical failure categories
What went wrong.
- Info access
- Accuracy (factual)
- Accuracy (honest)
- Delivery
- Authority
- Empathy
Layer02
EQ dimensions
How it was handled.
- Tone calibration
- Proportionality
- Intent alignment
- Resolution path
Layer03
Business-impact flags
What it cost you.
- blame_shift
- churn_signal_ignored
- cheerful_abandonment
- repeated_deflection
- escalation_ignored
- +14 more
Platform
One platform. Four health pillars.
Dollar impact per conversation
See exactly how much revenue is at risk from bad interactions — broken down by conversation, by agent, by week.
$0 at risk this week
Social threat detection
Track brand sentiment across conversations. Get alerted when frustration is about to spill onto social media.
72/1003 social threats
Surface issues customers report
Conversations reveal UX bugs your telemetry misses. See which site issues drive the most complaints.
Checkout timeoutx14
Mobile navx8
Payment 500x5
Rewrites, not platitudes
Every flagged conversation includes an exact rewrite — word for word, what the agent should have said.
"Thanks for reaching out! Is there anything else I can help with?"
"I understand this is frustrating. Let me get this to our team lead today so we can fix it properly."
Ranked product issues from conversations
Automatically surface and rank product complaints by volume. Know what to fix first, straight from your customers.
Widget Pro — sizing complaints
Dashboard — slow load reports
Mobile app — crash on checkout
What's at stake
The four headlines nobody wanted.
Each of these was a single bad conversation amplified. Legacy QA didn't catch them until the press did. Every card shows the AINGEL flag that would have fired first.
Air Canada2024
Chatbot invented a bereavement policy.
Lost in court. Set precedent: airline liable for AI-generated promises.
Would have fired
accuracy_honestHallucinated policy language — Layer 1 honesty check fires in <90s.
McDonald's2024
3-year IBM AI partnership, cancelled.
Viral TikTok failures. AI drive-thru pulled after millions of views.
Would have fired
repeated_deflectionIntent-alignment failure rate crossed threshold 4 months earlier.
DPD2024
Chatbot swore at a customer.
1.3M views on X. AI disabled same day. Brand damage: months.
Would have fired
brand_damageTone-calibration anomaly — single flag, critical severity, instant alert.
Integrations
Plugs into what you already use
Your helpdesk pipes conversations in. AINGEL scores them. Alerts go out through the channels your team already watches.
Helpdesks
Zendesk
Intercom
Gorgias
Salesforce
HubSpot
Freshdesk
Alerts
Slack
Gmail
WhatsApp
AI Agents
OpenAI
Claude
Gemini
The difference
Same conversation. Very different score.
Most QA tools check if the agent followed the script. AINGEL checks if the customer actually got what they needed.
100% of conversations scored
Not a 1-3% sample. Every single one.
EQ, not keyword matching
Tone, intent, proportionality, resolution path.
Revenue impact in dollars
Not just CX metrics. What it actually cost you.
Alerts in real time
Not a weekly report nobody reads.
Specific rewrites, not vague feedback
Word for word, what the agent should have said.
Research-backed methodology
Not a blank scorecard you configure yourself.
Your conversations already hold the answers.
Send us 100 transcripts. We'll run them through our EQ scoring and show you what your current QA tool didn't catch. Free, no strings.
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100 conversations free · No credit card required