Tropir is an autonomous LLM-Ops engineering platform designed to build and optimize AI pipelines by automatically diagnosing, repairing, and improving large language model (LLM) workflows. Tropir functions as an AI that not only detects where an LLM pipeline fails but also intervenes to fix issues and continuously iterates until the pipeline improves.
How does Tropir work?
Tropir analyzes the complete flow of logic across prompts, tools, and retrievals within an AI pipeline. When a failure occurs—such as hallucinations, inaccurate retrievals, or broken tool calls—Tropir traces the root cause, suggests targeted fixes, and reruns the pipeline with those adjustments. It evaluates the results to determine if the issue is resolved, repeating the process until improvements are achieved. Key features include:
- Full pipeline trace and failure forensics
- Automated root-cause analysis
- Self-improving agent capabilities
- Bottleneck detection
- Built-in evals and conversational interface
- Plug & Play integration for rapid deployment
What problems does Tropir solve?
Modern LLM-powered applications often encounter complex failures that are hard to diagnose and fix. Tropir addresses this challenge by providing an autonomous agent that not only traces where the pipeline broke but also actively fixes it and confirms the effectiveness of each change. This reduces manual debugging time, accelerates development cycles, and increases reliability for teams building with LLMs.
Who uses Tropir?
Tropir is designed for AI engineers, machine learning teams, and organizations building or maintaining complex LLM pipelines. While specific customer names are not publicly listed, its use case is particularly relevant for teams seeking automated solutions for pipeline reliability and performance in production AI systems.
How was Tropir started?
Tropir was founded in 2024 and is part of the Spring 2025 Y Combinator batch. The company is based in San Francisco, California, USA, and currently has a small, focused team of two employees.
Who leads Tropir?
The founding team includes Aarush Kukreja (Co-Founder), who previously worked at Duckie AI (YC W24), Princeton NLP, and Caltech SSPP, and holds a CS degree from Princeton; and Ayush Karupakula (Co-Founder), with experience at General Analysis (YC S24), Philips, UW NLP, and StartonAI, and a CS degree from Georgia Tech. Their backgrounds in AI research and production systems inform Tropir’s approach to autonomous LLM-Ops engineering.
What makes Tropir unique?
Tropir positions itself as the first autonomous engineer for LLM operations, going beyond traditional tracing tools by automatically fixing and re-evaluating pipelines. Its self-improving agent continuously iterates until the pipeline meets quality standards, providing an advanced, hands-off solution for managing AI workflow complexity.
Learn more about the product and its latest launch—including features like the Traceback Engine and Fix + Rerun functionality—on the Tropir website or watch their video introduction.
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