Voker

What it does

Voker is an analytics platform designed specifically for AI agents. It transforms raw agent interactions into structured, actionable insights that help teams understand agent performance, user behavior, and business impact. The platform automatically captures data from LLM calls, classifies user intents, detects corrections, and measures resolution rates. It provides dashboards and self-service analytics for product managers, analysts, and business teams without requiring engineering support for every query.

Voker offers lightweight SDKs for JavaScript/TypeScript and Python that integrate with major AI providers including OpenAI, Anthropic, Gemini, and frameworks like Vercel AI SDK, LangChain, and CrewAI. The SDK wraps existing LLM calls with minimal code changes—typically swapping imports and adding agent and session identifiers. For unsupported frameworks, Voker provides an HTTP-based event API.

Key features include agent performance monitoring (detecting silent failures), user insights (behavioral profiles per user), Smart Skills (auto-generated skills that improve from failures), cost optimization (calculating ROI per agent), and automated annotations (discovering intents, corrections, and resolutions).

Who it is for

Voker is built for teams that run high-volume conversational AI agents—typically 1,000+ chat sessions per month—with complex multi-turn conversations involving tools, RAG, and MCP. The target users are cross-functional teams including developers, product managers, analysts, and business stakeholders who need agent insights without constant engineering involvement.

The platform is especially relevant for companies that have already deployed AI agents in production and are struggling to measure their effectiveness, identify failure modes, or prove ROI to leadership. Testimonials on the website come from roles like CTO, CEO, and co-founder at companies such as Lightfield, True Classic, and Dutch.

Why it matters

As AI agents move from prototypes to production, teams face a new set of challenges: they don't know if agents are actually helpful, accurate, or hitting walls. Traditional logging and tracing don't tell you whether an agent resolved a user's intent or just generated a plausible-sounding response. Getting useful usage data is resource-intensive, and proving agent ROI is difficult because usage stats don't connect to conversion, retention, or revenue.

Voker addresses these gaps by providing structured analytics that correlate conversational data with business outcomes. It helps teams detect issues before customers complain, iterate on agent improvements faster, and quantify the value of AI investments. The platform aims to give product and business teams the same level of visibility into agent performance that they have for other parts of their product.

Launch signal

Voker launched as a Y Combinator S24 startup. The company's public launch on Hacker News positions it as "Analytics for AI Agents," targeting the growing need for observability and measurement in the agentic AI space. The website is live with documentation, SDKs, and a dashboard, indicating the product is available for use.

Brand and naming

The name "Voker" is short, punchy, and evokes "vocal" or "voice," fitting for a product that analyzes conversational AI. It's easy to remember and spell, which helps with word-of-mouth and search. The tagline "Analytics for AI Agents" clearly positions the product in the developer tools and AI observability category, differentiating it from general-purpose analytics platforms.