<p>AI-powered conversations have moved from experiments to core product surfaces. In-product copilots, support assistants, and autonomous agents now handle a meaningful share of how users experience software. For product teams, this shift creates a new problem space. Conversations are no longer just interactions — they are continuous feedback loops. Every question, hesitation, correction, or escalation contains information about product clarity, usability, gaps, and value. Yet most teams lack a systematic way to understand this signal at scale. This document explains the thinking behind Cipher’s architecture from a **product and outcomes perspective** — not an implementation deep dive, but the decisions that make conversation intelligence usable, adaptable, and trustworthy for real teams. * * * ## The Product Problem Cipher Solves Product teams deploying AI assistants face a familiar set of frustrations: - You can see *usage*, but not *experience* - You know something feels off, but not *where or why* - You ship prompt or model changes without knowing their real impact - Feedback is scattered across conversations instead of flowing into the roadmap Traditional analytics were built for clicks and screens. Conversation-based products need a different abstraction — one that treats dialogue as first-class product data. Cipher exists to close this gap. * * * ## Conversations as Product Signals A core product decision in Cipher is to treat conversations as **structured experience signals**, not raw transcripts. From a product lens, teams don’t want to read conversations — they want answers to questions like: - Are users getting what they came for? - Where do they get confused or frustrated? - Which parts of the product create the most conversational friction? - Which assistant changes actually improve outcomes? Cipher’s architecture is designed to surface these answers consistently, without requiring manual review or custom engineering. * * * ## Separation of Responsibilities, Not Complexity One of the biggest risks in analytics systems is overfitting logic to today’s definition of success. Product reality changes constantly. Cipher is intentionally structured so that: - Understanding conversations - Interpreting patterns - Judging outcomes are **separate responsibilities**. From a product standpoint, this means teams can evolve *what they care about* without breaking the system. A support team may focus on resolution and cost. A product team may focus on confusion, adoption, and missing capabilities. Leadership may care about trend direction and risk. The same conversation data supports all of these views. * * * ## Metrics That Can Evolve with the Product Product metrics are not static. As products mature, teams refine how they define quality, success, and impact. Cipher is designed so that metrics: - Are configurable, not hardcoded - Can depend on other signals safely - Can be tested, compared, and versioned over time This enables product teams to: - Experiment with different definitions of quality - Compare prompt or model changes objectively - Roll out improvements gradually and measure real impact From a product perspective, this turns conversation analysis from a reporting tool into a learning system. * * * ## Built for Multiple Teams, Not Just One Persona Conversation intelligence touches many stakeholders: - Product teams care about usability and feature gaps - Support teams care about resolution and efficiency - AI teams care about behavior across models and prompts - Leadership cares about risk, cost, and experience quality Cipher’s architecture supports this by allowing different teams to work from the same underlying data, while applying their own lenses, thresholds, and priorities. This avoids the common failure mode where every team builds its own partial view of the same conversations. * * * ## Scales with Usage and with Expectations From a product standpoint, scalability is not just about volume — it’s about expectations. As conversation volume grows, teams expect: - Faster feedback cycles - Near real-time visibility into issues - Historical context for decisions - Confidence that insights are consistent Cipher is designed to scale horizontally and operate asynchronously, ensuring that analysis keeps pace with usage without blocking core product workflows. The result is a system that remains useful whether a product handles hundreds or millions of conversations. * * * ## From Insights to Action Perhaps the most important product principle behind Cipher is this: &gt; Insight without action is just noise. Cipher is built to connect conversational insight directly to product decision-making — highlighting what to fix, what to improve, and where teams should focus next. Instead of drowning in transcripts or dashboards, teams get a prioritized understanding of what conversations are telling them about the product. * * * ## Why This Matters As AI assistants become permanent product surfaces, conversation quality becomes product quality. Teams that can systematically learn from conversations will: - Build clearer, more intuitive products - Ship AI experiences with confidence - Catch issues before they escalate - Align product, CX, and AI efforts around real user behavior Cipher is designed as the intelligence layer that makes this possible — not by adding complexity, but by giving product teams the visibility they’ve been missing. * * * *Cipher is part of the Lexsis platform, focused on helping teams understand what their users are actually telling them — at scale, and in context.*</p>