Over the past year, AI assistants have transitioned from experimental pilots to core infrastructure. They now reside within SaaS platforms, mobile apps, onboarding flows, and enterprise workflows, autonomously resolving issues and guiding users through complex tasks.
However, as adoption has scaled, a new challenge has emerged. These assistants are generating thousands—sometimes millions—of conversations. Within this unstructured data lies a wealth of insight: patterns, frustrations, misunderstandings, and signals regarding product gaps.
Yet, this data remains largely untapped. This observation led us to a critical realization: while we have mastered the art of deploying AI, we lack the infrastructure to understand it.
The Analytics Gap: Measuring Performance, Missing Intent
Consider the current analytics ecosystem. It is robust, yet incomplete regarding conversational interfaces:
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LLM Observability tools measure technical performance: latency, token usage, and model drift. However, they do not reveal if the user actually achieved their goal.
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Product Analytics tools track behaviors: funnels, drop-offs, and clicks. They remain blind to conversational nuance.
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Support Platforms analyze human agent performance, often ignoring the vast volume of autonomous AI interactions.
We have excellent tools for understanding what users do, but we lack adequate tools for understanding what users say. Conversations are the most honest source of feedback, revealing confusion, feature requests, and sentiment shifts. By ignoring them, companies remain blind to the "why" behind their metrics.
The Insight: The New UI Layer Requires a New Analytics Layer
We consistently heard the same feedback from product teams: they deployed an assistant but had no visibility into its effectiveness. They could not pinpoint where users were getting stuck or if the answers provided were accurate.
AI assistants represent a new UI layer, but the analytics stack has not kept pace. Clicks have evolved into conversations; user journeys are now dialogue flows.
We built Cipher to bridge this gap. It is a conversation intelligence engine designed specifically for modern AI assistants.
Turning Dialogue into Data
Cipher ingests assistant conversations and transforms raw, unstructured dialogue into clean, actionable intelligence. It automatically extracts and categorizes insights across five key dimensions:
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Friction & Confusion: Identifying repeated queries, clarification loops, and abandonment moments where the user gives up.
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Assistant Failures: Detecting hallucinations, misunderstood intents, and context retrieval errors where the AI provides the wrong answer.
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Product Gaps: Surfacing hidden feature requests and patterns in unmet needs that users are explicitly asking for.
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Sentiment Analysis: Monitoring tone shifts, frustration signals, and confidence levels throughout the chat.
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Emerging Trends: Highlighting recurring issues or breakdowns following new feature releases.
Cipher converts these findings into prioritized insight cards, complete with evidence and confidence scores. Instead of forcing you to read through thousands of logs, it routes high-priority issues directly to the tools your team already uses, like Jira, Linear, or Slack.
Privacy by Design
We recognize that conversational data often contains sensitive information. Cipher is built with a security-first architecture. We utilize PII reduction at ingestion, optional metadata-only modes, and region-based data residency to ensure that only the minimum necessary information is processed to generate insights.
The Future of Assistant Analytics
Cipher is designed for SaaS products, consumer apps, and enterprise teams utilizing AI assistants. If your team has ever struggled to quantify the performance of your AI assistant—or if you simply don't know why users are dropping off—Cipher provides the clarity required to move from guesswork to optimization.
The era of blind AI deployment is over. It is time to start listening to what your users are actually saying.
