Support teams rarely struggle with volume alone. The real challenge is consistency, compliance, and visibility across every interaction. Robylon analyzes 100% of conversations across chat, voice, email, WhatsApp, Instagram, Facebook, and ticketing systems, and assigns structured quality and compliance scores in real time.
This is not sampling. Every conversation is scored.
Full Conversation Scoring at Three Levels
Robylon runs automated evaluation across three distinct dimensions for every interaction
- Customer sentiment analysis
- Human agent handling quality
- AI agent handling quality
Scores are aggregated at the dashboard level, enabling performance comparisons by agent, team, channel, or timeframe
This allows teams to answer operational questions such as:
- How did Hannah perform on voice tickets last month versus Tim?
- Is CSAT lower on WhatsApp than email?
- Are AI agents matching human performance benchmarks?
The analysis runs weekly across all historical conversations, identifying systemic issues across product, operations, or SOP gaps.
AI-Powered Evals for Quality and Compliance
Robylon’s Evals engine automatically assesses conversation quality, customer sentiment, and key outcomes without manual QA review.
Teams can:
- Automatically score conversation quality
- Detect recurring issue patterns
- Flag compliance risks
- Surface operational bottlenecks
- Identify missing SOPs or knowledge gaps
The system categorizes insights into:
- Product issues
- Operational inefficiencies
- SOP or knowledge base gaps
This creates a closed loop between support execution and process improvement.
Accuracy Benchmarks Across Channels
Robylon combines automated scoring with human oversight to maintain enterprise-grade accuracy.
Key performance metrics across deployments:
- 100% query coverage with AI + human model
- 99% accuracy from day one with human oversight
- 93% improvement in resolution time
- 43% fewer repeat inquiries
- 90% average CSAT
For voice:
- 95%+ intent classification accuracy after two weeks of training
- 800ms to 1 second latency for English speech-to-intent
- 5% word error rate for English accents
For ticket automation:
- 83% automated resolution at 93% accuracy in high-complexity trading workflows
- Accuracy improved from 93% to almost 100% using human-in-the-loop escalation
Scoring and compliance checks apply to both AI and human agents.
Explainability and Debug Logs
Robylon exposes AI decision logs on select enterprise plans.
For any conversation, teams can see:
- Reasoning steps
- Routing logic
- Tokens used
- Latency
- Workflow path
Version history is maintained for workflows and AI agents. Every publish creates a named snapshot that can be reviewed or restored.
This enables:
- Audit traceability
- Rollback of problematic updates
- Structured compliance review
Compliance-Ready Governance
Robylon follows SOC 2, GDPR, CPRA, and HIPAA frameworks.
Internal ISMS aligns with ISO 27001 and SSAE 18 SOC 2 standards.
Security controls include:
- AES-256 encryption at rest
- TLS 1.2+ encryption in transit
- WORM-style immutable storage for regulated logs
- Exportable audit logs in regulator-ready formats
- Configurable PII masking in dashboards
For finance workflows:
- Dynamic routing based on risk score thresholds
- Step-up authentication via chat, WhatsApp, text, or voice
Guardrails prevent unsafe medical advice and enforce no-diagnosis boundaries in health use cases.
Multi-Channel Quality Scoring
Robylon maintains shared context across:
- Chat
- Voice
- Ticketing systems
Unified Inbox aggregates conversations across channels, with configurable metrics and advanced filters for QA review.
New analytics capabilities allow:
- Custom timeframes
- Human vs AI performance comparison
- Channel-level CSAT tracking, including WhatsApp
Real-World Scale Examples
Online trading platform
- 300k+ tickets annually
- 600+ use cases consolidated into 15 structured issue types
- 80%+ automated resolution
- ~25% reduction in support cost within 6 months
Logistics delivery platform
- 52% tickets auto-resolved within minutes
95% improvement in AHT
- 30% cost savings
Ecommerce chat automation
- 94%+ chats handled by AI
- 80% automated resolution rate
- 93% improvement in resolution time
HealthifyMe pre-sales
- Conversions increased from 12–14% to 18%
- 40% growth in conversions
Deployment and Integration
Robylon integrates natively with:
Zendesk, Freshdesk, Zoho, Intercom, Slack, Shopify, Salesforce, HubSpot, Amazon Connect, Aircall, WhatsApp, Microsoft Teams, Zapier, and more
Deployment options include:
- AWS-hosted infrastructure
- Region-specific local deployments across US, UK, EU, GCC, India, SEA
- Self-hosted and private cloud deployments
SMBs can go live in under 30 minutes using a simple code snippet deployment for Shopify or WordPress.
Enterprise deployments typically involve structured SOP ingestion, API integrations, and multi-agent orchestration.
Continuous Improvement Loop
Robylon does not treat scoring as a reporting layer. It connects scoring to action.
Capabilities include:
- AI-generated insights summarizing recurring issues
- Bulk knowledge base testing via CSV uploads
- Workflow version management with instant rollback
- AI Copilot to improve knowledge base quality in real time
Every conversation feeds structured data back into system-level intelligence.
Enterprise Pricing and Scale
Robylon offers:
- Per-resolution pricing
- Usage-based pricing
- Free tier for SMBs with 250 ticket credits
- Average enterprise contract around $30,000 annually resolving 3,000–4,000 queries per month
This aligns scoring, automation, and cost efficiency into a single operational layer.
Robylon delivers software that scores every support conversation for quality and compliance across channels, detects operational risk in real time, and connects evaluation directly to workflow improvement.
👉 Learn more at Robylon.ai
FAQs
How is AI conversation scoring different from traditional QA sampling?
Traditional QA reviews only 1–5% of conversations manually, which can miss compliance risks and performance gaps. AI conversation scoring evaluates 100% of interactions automatically, flags violations in real time, detects sentiment trends, and provides audit-ready explanations for each score deduction. This reduces bias, improves coaching accuracy, and ensures complete compliance monitoring across channels.
Can AI really score 100% of support conversations accurately?
Yes. Modern AI QA platforms use a combination of large language models (LLMs), NLP, and rule-based logic to analyze every chat, voice transcript, and email — not just samples. Scoring can be calibrated against human QA teams, customized with weighted scorecards, and continuously improved using feedback. This enables full coverage with consistent, explainable evaluation across quality, compliance, and sentiment.
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