Opaline

Product Introduction

1. Definition: Opaline is a team-wide analytics and observability platform specifically designed for AI-powered coding agents, such as Anthropic's Claude Code and Codex. It operates as a SaaS (Software-as-a-Service) tool that integrates via a CLI (Command Line Interface) to provide granular, message-level telemetry.
2. Core Value Proposition: Opaline exists to solve the "black box" problem in AI-assisted software development. It provides engineering leaders and developers with actionable insights into AI coding session performance, enabling data-driven decisions to optimize token usage, reduce AI coding costs, and improve team skill development by analyzing every interaction.

Main Features

1. Message-Level Session Analytics: Opaline captures and analyzes every single message exchange within a Claude Code or Codex session. This includes user prompts, AI agent responses, and tool calls. For each message, it logs precise timestamps, token counts (input/output), and latency, creating a complete audit trail of the development session.
2. Token Cost and Efficiency Tracking: The platform automatically calculates the estimated cost for every session and aggregates it across the team. It breaks down cost by user, project, and time period, allowing managers to identify token usage trends and pinpoint inefficient prompting patterns or over-reliance on expensive model contexts.
3. Skill and Tool Usage Intelligence: Opaline categorizes and tracks the specific "skills" or tools invoked by the AI agent during a session (e.g., file editing, terminal commands, code review). This provides visibility into developer-AI collaboration patterns, showing which capabilities are most used and where developers or the AI may be struggling, turning qualitative frustration into quantitative data.

Problems Solved

1. Pain Point: Engineering teams lack visibility into how AI coding assistants are actually used, leading to uncontrolled cloud costs, unmeasured productivity gains, and missed learning opportunities from failed or inefficient interactions.
2. Target Audience: Engineering Managers and Tech Leads who need to manage AI tooling budgets and team productivity; Software Developers and DevOps Engineers who want to optimize their personal prompting strategies and learn from collective team patterns.
3. Use Cases: Cost Attribution and Budgeting for AI coding tools across multiple projects. Onboarding and Training new developers by showing them effective prompt patterns from senior team members. Performance Benchmarking different AI models or prompting techniques to establish best practices. Identifying Blockers by analyzing sessions with high latency or repeated tool errors.

Unique Advantages

1. Differentiation: Unlike generic AI usage dashboards from cloud providers, Opaline specializes in the coding agent workflow, offering message-level granularity that tools like the Anthropic Console or OpenAI's usage dashboard do not provide. It focuses on team-wide analytics rather than individual user metrics.
2. Key Innovation: Its core innovation is the fine-grained parsing and categorization of AI agent "skills" within a coding session. By moving beyond simple token counting to semantically understanding the type of AI assistance being requested (e.g., "debugging," "refactoring," "documentation"), it provides unique insights into the development process itself.

Frequently Asked Questions (FAQ)

1. How does Opaline track Claude Code sessions? Opaline uses its open-source CLI tool that developers run locally. The CLI securely forwards anonymized, message-level telemetry data from your Claude Code or Codex sessions to the Opaline dashboard, providing analytics without accessing your source code.
2. Is Opaline compatible with other AI coding assistants like GitHub Copilot or Cursor? Currently, Opaline is specifically optimized for Anthropic's Claude Code and Codex agents. Its deep integration with Claude's skill/tool-calling architecture allows for its unique analytics. Support for other agents may be developed based on demand.
3. What data does Opaline collect, and is my code secure? Opaline is designed with privacy in mind. It focuses on collecting metadata about the session (timing, token counts, skill types used, error states) and does not store or transmit your actual source code or proprietary business logic. The data is anonymized and aggregated for team-level analysis.
4. How can Opaline help reduce our AI coding costs? By providing visibility into token consumption per message, session, and user, Opaline helps identify inefficient prompting habits, unnecessary long context windows, or overuse of costly model features. Teams can use this data to create cost-effective prompting guidelines and training.
5. Can Opaline measure developer productivity gains from using AI? While not a direct productivity meter, Opaline provides proxy metrics like session completion time, iteration cycles, and skill success rates. By analyzing trends, teams can correlate effective AI use with faster task resolution and improved code quality, offering a data-backed view of AI's impact.