Cipher: Conversation Intelligence

Cipher is the Conversation & Experience Intelligence Layer for AI agents. As organizations deploy AI copilots, support bots, and agentic workflows, Cipher provides the analytics and UX intelligence required to systematically improve how agents interact with users.

Note on Terminology: Throughout this documentation, we use the terms "AI agent", "AI assistant", and "voice bot" interchangeably. They all refer to the same concept: autonomous AI systems that interact with users through conversation, whether text-based or voice-based.

What is Cipher?

Cipher analyzes AI agent conversations to answer three critical questions that no existing analytics or observability tool can:

1. What are users actually telling your agent? (UXI – User Experience Intelligence)

2. How is your agent behaving across different models, prompts, and configurations?

3. Which improvements will increase resolution rate, reduce frustration, and lower cost per ticket?

Cipher transforms raw conversations into structured insights, benchmarks, and actions that help product, CX, and ML teams understand agent performance end-to-end.

How It Works

Cipher processes AI agent conversations through an automated analysis system:

1. Conversation Ingestion: Conversations are ingested from your AI agent deployments including in-product copilots, support bots, agentic workflows, customer service automation, and voice bots.

2. Analysis: Cipher extracts signals from conversations, identifies patterns, and performs quality assessments to understand conversation effectiveness.

3. Model Benchmarking: Cipher evaluates agents across model choices, prompt versions, and configurations to help you compare performance and optimize settings.

Key Features

User Experience Intelligence (UXI)

Deep analysis of how users experience your agent:

• Frustration Score: Measures user frustration levels

• Confusion Loops: Identifies repeated clarifications and misunderstandings

• Drop-off Indicators: Flags where users abandon conversations

• Intent Misunderstanding: Detects when the agent misunderstands user intent

• Tone Mismatches: Identifies when agent tone doesn't match user expectations

• Sentiment Progression: Tracks how sentiment changes throughout the conversation

• Resolution Status: Determines if conversations were resolved or unresolved

Experience Quality Score (XQS)

A unified 0-100 score that measures conversation quality. Higher scores indicate better user experiences. XQS considers:

• Resolution success

• Conversation efficiency

• User satisfaction signals

• Frustration levels

• Turn count

Model Benchmarking

Compare different models and configurations:

• Model Comparison: Which model (GPT, Claude, Gemini) produces the best user experience?

• Resolution Rates: Which model yields higher resolution rates?

• Confusion Patterns: Which model causes more confusion loops?

• Intent Understanding: Which model misunderstands certain intents?

Prompt Optimization

Measure how prompt changes affect user satisfaction:

• Misunderstanding rate changes

• Average frustration score impact

• Drop-off rate changes

• Conversation length changes

• User satisfaction signal changes

Signal Extraction

Automatically identifies frustration patterns, confusion indicators, drop-off points, resolution quality, and operational efficiency metrics.

Operational Metrics

Track operational efficiency:

• Cost per Ticket: Cost per resolved conversation

• Cost per Escalation: Cost per escalated conversation

• Model Cost Comparison: Compare costs across models (e.g., GPT-4o-mini vs Claude Sonnet)

• Automation Success Rate: Percentage of conversations successfully automated

• Handoff Accuracy: Quality of escalations to human agents

• Escalation Predictors: Factors that predict when escalation is needed

Use Cases

Cipher is ideal for:

• AI Agent Optimization: Identify where users get stuck and improve resolution rates

• Support Bot Improvement: Measure bot effectiveness and optimize handoff logic

• In-Product Copilot Analysis: Understand user interactions and optimize experience

• Model Comparison: Compare GPT, Claude, Gemini, and custom models to find the best fit

Privacy & Safety

Cipher applies strict guardrails:

• PII masking at ingestion

• Metadata-only mode for sensitive organizations

• Region-based data residency

• Versioned insights with audit trails

• Long-message truncation with controlled summarization

This allows enterprise-grade safety without losing insight quality.

Coming Soon: Framework Integrations

We're building integrations for various AI agent frameworks that combine observability with experience intelligence directly within Cipher. These integrations will provide:

Unified Observability + Experience Intelligence

Combine technical metrics (latency, tokens, errors, model versions) with user experience quality (frustration, resolution, satisfaction) in one unified platform. No more switching between observability tools and experience analytics.

Framework-Specific Integrations

Native integrations for popular AI agent frameworks will enable:

• Automatic conversation ingestion

• Seamless setup with your existing observability stack

• Real-time experience intelligence alongside technical metrics

• Framework-specific optimizations and insights

Benefits

• Complete Visibility: See both technical performance and user experience in one place

• Faster Debugging: Correlate technical issues with user experience problems

• Better Optimization: Understand how model changes affect both technical metrics and user satisfaction

• Unified Analytics: Single source of truth for agent performance

Stay tuned for updates on framework integrations and observability partnerships!

Next Steps

• Product Comparison - See how Cipher compares to VOCx

• Getting Started - Set up Cipher for your AI agents