Voker is an analytics platform for the era of AI agents, designed to help teams monitor and improve AI agents running in production environments.
Purpose The core problem Voker solves is this: scanning traces alone cannot determine whether an agent is helpful, accurate, or encountering bottlenecks, and teams often discover problems only after customer complaints or a spike in churn. Voker turns AI agent interactions into structured analytics, enabling product managers, analysts, and business teams to self-serve understandable insights without submitting tickets or waiting for engineering resources.
Key capabilities
- Track what AI agents say to users, identify knowledge gaps, detect anomalies, and measure improvement over time.
- Connect agent metrics with business outcomes: by correlating conversation data with already collected user data, link agent performance to conversion, retention, or revenue.
- Automatically classify user goals: identify what users want from natural conversations.
- Detect friction: surface issues early when users do not get the results they need, before they turn into churn.
- Identify resolution: determine whether the agent resolved user intent and measure the success rate of each interaction.
- Team self-serve insights: product, analytics, and business teams can directly obtain the insights they need, no longer creating an engineering bottleneck.
How to use Voker offers lightweight integration, requiring only a few lines of code to start gaining insights without changing infrastructure. The platform supports any LLM framework or model and provides seamless integration with OpenAI, Anthropic, and Gemini. The platform emphasizes no vendor lock-in, with data owned by the user. Users can start for free, scale as their agent grows, and invite their entire team to join.
Use cases Voker is aimed at teams building first-class agent products, covering every stage of the agent journey: teams experimenting with adding agents to their products can capture analytics data from day one; teams that have launched agents but still have limited usage; teams whose agents have become central to the product experience and are frequently relied on by users; and teams running agents at scale, where reliability, optimization, and governance are critical.
Limitations The material does not provide specific information such as pricing, plan details, technical architecture, supported languages, or deployment methods; detailed documentation on the specific implementation mechanisms and integration steps of some features is not yet available.
