SailFar provides a private, continuously updating benchmark platform that simulates an AI agent’s real-world environment to evaluate its performance across text, voice, image, and video modalities. The service runs full‑stack simulations, generates detailed failure reports, and even creates fix pull requests, enabling teams to quickly identify and remediate issues such as hallucinations or tool misuse before deployment.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents are typically evaluated using public benchmarks or ad‑hoc tests that do not reflect the specific production environment, leading to gaps in performance, hallucinations, and tool misuse. Building custom synthetic simulations is time‑consuming, and existing evaluators often drift, especially for multimodal outputs such as voice, image, and video.
Solution
SailFar offers a private, continuously evolving benchmark platform that replicates an organization’s real production environment for AI agents. The service runs a comprehensive suite of 237 scenarios—including common workflows and adversarial edge cases—within a managed cloud sandbox that isolates testing from live data. It automatically generates detailed evaluation reports covering hallucination rates, tool efficiency, tone alignment, and multimodal quality metrics, and highlights specific failures for rapid remediation. By mapping the agent’s tool surface and environment, SailFar provides end‑to‑end testing for text, voice, image, and video agents, enabling teams to iterate quickly and align agents with real‑world expectations.
Target Audience
Primary customers are product and engineering teams that develop and deploy AI agents—such as customer support bots, virtual assistants, and multimodal conversational systems—within enterprises that require reliable, production‑aligned testing.
Features
- Private, continuously updated benchmark that mirrors the customer’s production stack (e.g., FastAPI, LangChain, Postgres, 31 tools)
- 237 pre‑built scenarios spanning happy paths, edge cases, and adversarial conditions
- Automated metrics for hallucination rate, tool efficiency, tone alignment, and multimodal quality (voice tone, visual fidelity, etc.)
- Detailed failure reports that pinpoint exact scenario breakdowns and suggest code changes
- Managed cloud sandbox that runs simulations without accessing or affecting live production data
- End‑to‑end evaluation of text, voice, image, and video agents, including output tracing and outcome verification