Support teams do not struggle because they lack data. They struggle because they cannot interpret it at scale.
Robylon analyzes every support interaction across chat, email, ticketing, WhatsApp, Instagram, Facebook, and voice. Each ticket is evaluated on customer sentiment, human agent handling quality, and AI agent performance. The result is a unified performance layer that turns raw conversations into measurable operational insight.
Analyze Every Ticket, Not Just a Sample
Robylon runs automated analysis on 100% of tickets across channels. Each interaction is evaluated across three layers:
- Customer Sentiment Analysis
- Human Agent Handling Quality
- AI Agent Handling Quality
This applies to conversations from Zendesk, Freshdesk, Zoho, Intercom, Slack, WhatsApp, Instagram, and more.
Sentiment Analysis at Scale
Robylon detects:
- Customer sentiment at ticket level
- Emotion and tone shifts during the conversation
- Weekly sentiment trends
- Escalation signals
For voice conversations, sentiment and tone detection are supported directly within the voice stack.
Sentiment outputs are aggregated into dashboards that show performance over time, per channel, and per agent.
Agent Handling Quality Scoring
Every human agent’s performance is scored across the tickets they handle. Robylon aggregates quantitative metrics across a defined time period and generates per-agent performance scores.
You can see:
- Total tickets handled per agent
- Average sentiment score of conversations handled
- Handling quality scores
- AI vs human comparison metrics
Quantitative scoring exists at the agent level, while deeper qualitative coaching insights are supported at system level via AI insights.
Built-In QA & Evaluation Engine
With the launch of Evals, Robylon automatically assesses:
- Conversation quality
- Customer sentiment
- Key outcomes
- Trends over time
No need to manually review hundreds of interactions.
New analytics upgrades allow teams to:
- Track human agent performance alongside AI agents
- Filter conversations by CSAT, channel, agent, duration, tags
- Export transcripts for audit and QA review
Weekly AI Insights: Product, Ops, and SOP Gaps
Once every week, Robylon runs an AI analysis across historical conversations and generates system-level insights.
Insights are categorized into:
- Product Issues
Example: 40% of complaints this week relate to a specific product line, significantly higher than previous weeks. - Operational Bottlenecks
Example: Follow-up delays increased due to slower second-response times. - SOP or Knowledge Gaps
Example: Customers are asking about updated pricing not reflected in the knowledge base.
This transforms ticket data into actionable operational decisions.
Accuracy Benchmarks Across Channels
Robylon’s AI agents reach:
- 93% accuracy on ticketing side today, with ongoing improvements
- 98% accuracy on chat in production environments
- 95%+ voice intent classification after 2 weeks of training
In a large online trading platform processing 300k+ tickets annually, Robylon:
- Automated 83% of tickets within 15 days
- Achieved 93% accuracy initially
- Reached near 100% accuracy with human-in-loop validation
- Reduced support costs by ~25% in 6 months
In ecommerce deployments:
- 85% chat automation
- 60% ticket automation
- 3–6 second average reply time
Unified Performance Dashboard
Robylon provides a unified dashboard across chat, voice, and ticketing.
Recent upgrades include:
- 15+ advanced inbox filters (channel, CSAT, duration, tags, agent)
- Human + AI performance comparison widgets
- Transcript export and sharing
- Bulk testing of knowledge base responses
- Debug logs exposing reasoning, latency, and token usage
This makes QA, compliance, and performance monitoring continuous rather than reactive.
Enterprise-Grade Governance & Compliance
Robylon’s infrastructure is enterprise-ready (SOC 2, GDPR, CPRA / CCPA, HIPAA)
The internal Information Security Management System aligns with ISO 27001 and SSAE 18 SOC 2 frameworks.
Security features include:
- AES-256 encryption at rest
- TLS 1.2+ in transit
- WORM-style immutable storage
- Audit log exports
- SSO / SAML support
- Region-based deployment (US, UK, EU, GCC, India, SEA)
For regulated environments, audit logs can be exported in regulator-ready formats.
Real Results Across Industries
Robylon supports all industries including ecommerce, logistics, SaaS, fintech, health & fitness, trading platforms,...
Examples:
- 93% improvement in resolution time in ecommerce deployments
- 25% reduction in support costs for trading platform
- 52% automated resolution within minutes in logistics ticketing
- 35% reduction in lead qualification costs using AI voice agents
- 40% growth in conversions via AI-driven pre-sales orchestration
Built for Enterprise and Self-Serve
Robylon supports:
- Enterprise deployments averaging ~$30,000 annually
- 3,000–4,000 queries per month per mid-market client
- Free tier with 250 ticket credits
- Pro plan at $35/month
- Business plan at $179/month
Deployment can be live in under 30 minutes for SMBs.
From Ticket Data to Operational Intelligence
Every ticket becomes:
- A sentiment data point
- A QA score
- A compliance artifact
- A product signal
- An operational metric
Instead of sampling 1–2% of conversations for manual QA, Robylon evaluates all of them.
Support teams gain:
- Full coverage scoring
- Quantified agent performance
- Weekly AI-generated insights
- Cross-channel visibility
- Governance-ready audit logs
Customer support stops being a cost center and becomes a measurable operating system for the business.
FAQs
Does Robylon provide system-wide insights beyond individual ticket analysis?
Yes. In addition to ticket-level scoring, Robylon runs a weekly AI-powered insights analysis across historical conversations to detect recurring issues. These insights are categorized into product issues, operational bottlenecks, or SOP/knowledge gaps, helping teams proactively fix root causes rather than reviewing tickets manually.
Can Robylon compare performance across different agents or teams?
Yes. Robylon analyzes every ticket at three levels: customer sentiment, human agent handling quality, and AI agent handling quality, and aggregates this into agent-level dashboards. You can view overall performance scores across all tickets handled by a specific agent over a defined time period, making it easy to benchmark agents against each other quantitatively.
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