Best Tool for Annotating LLM Outputs? Meet Besimple AI

In an AI-first world where Large Language Models (LLMs) and agent-based systems are rewriting how products are built, one constant remains: great models require great data. Yet, despite all the technological advancement in generative AI, data annotation — a critical pillar of model improvement — remains stuck in the past.

Enter Besimple AI, a startup from the Spring 2025 Y Combinator batch based in Redwood City, California. Founded by Yi Zhong and Bill Wang, two former Meta engineers who built annotation systems for the Llama models, Besimple AI aims to make high-quality, human-in-the-loop data annotation effortless, fast, and intelligent.

Besimple allows AI teams to spin up a custom data annotation platform in under a minute, transforming raw data into structured training pipelines — complete with dynamic interfaces, tailored guidelines, and AI-augmented reviewers. It is already being used by companies like Edexia, an AI grading startup, to power high-stakes model evaluation loops.

Why Is Data Annotation Still a Problem?

Despite the advancements in AI tooling, annotation workflows haven’t caught up. Most teams still rely on brittle spreadsheets, mismatched tools, and rigid processes that don’t scale with LLM complexity or multimodal data formats.

Here are the three main pain points most AI builders face:

Outdated Tools for Complex AI Workflows: AI teams are working with dynamic LLM outputs, agent traces, and multimodal data — yet annotation is often managed in Google Sheets or clunky legacy platforms not built for such diversity.
Bottlenecks in Human Review: As models improve, the data that matters becomes more nuanced. Only skilled reviewers — often the actual product team — can reliably annotate these cases. But the burden quickly becomes overwhelming.
No Feedback Loop from Production: Once a model is in production, real-world failure cases often don’t get annotated or reintegrated into evaluation pipelines. That’s a lost opportunity for rapid iteration and model improvement.

In short, annotation is where AI development breaks down.

How Does Besimple AI Solve It Differently?

Besimple AI doesn’t just patch existing problems—it rethinks annotation from the ground up. Here’s how it changes the game:

Instant Custom UI Generation

Traditional platforms require developers to spend hours building annotation interfaces that match their task. With Besimple, that’s history. The platform auto-generates a custom annotation interface based on the uploaded data, whether it's text, chat logs, audio, video, or even complex LLM traces.

And as the task evolves, teams can adjust the interface in seconds. No code. No plugins. Just data in, UI out.

Tailored Annotation Guidelines

Guideline creation is often overlooked but is critical to annotation quality. Besimple lets teams import existing guidelines or auto-generates new ones based on task goals, ensuring alignment from the start. These guidelines are embedded directly into the annotation flow for real-time reference.

AI Judges for Human-in-the-Loop Review

At the heart of Besimple lies its LLM-powered “AI Judges.” These smart agents learn from human reviewers and begin handling simple annotation tasks themselves. They evaluate production data in real-time and escalate only the ambiguous or high-impact edge cases.

This blended approach creates an efficient, scalable human-in-the-loop system, where humans focus only on what matters most.

Enterprise-Grade Flexibility

Besimple isn’t just built for scrappy startups. It’s designed for enterprise deployments, offering:

On-premise installation options
Granular user roles for internal SMEs, external vendors, or Besimple’s own annotator network
Secure workflows and governance for sensitive data

This makes Besimple a viable solution even for industries with strict compliance needs.

Lightning-Fast Setup

Unlike traditional solutions that require integration cycles and custom engineering, Besimple onboards teams in 60 seconds. Drop your data, pick or import your guidelines, and start labeling instantly. That’s not a slogan — that’s the core product promise.

Who Is Behind Besimple AI?

Bill Wang and Yi Zhong bring unmatched credibility to the table.

Bill Wang previously launched five products at Meta (and retired two) and has a history of scaling mobile products to hundreds of thousands of users. His blend of product thinking and technical execution is rare.
Yi Zhong is a seasoned AI product leader who’s held roles at Microsoft, Dropbox, and Meta. He’s led deployments of AI systems at scale, always with a focus on connecting technical solutions to business value.

Together, they built Meta’s annotation system for Llama, Meta’s flagship open-source LLM series. Their platform accelerated model iteration and drastically improved training quality — lessons they’ve now productized into Besimple.

Who Is Using Besimple Today?

Besimple is already powering training and evaluation pipelines for leaders in:

Customer Support
Search and Relevance
Education and EdTech
Conversational AI

One early flagship customer is Edexia, a fast-growing AI grading startup. Using Besimple, Edexia annotates hundreds of model outputs, improves its evaluation accuracy, and creates a feedback loop that strengthens its grading engine weekly.

Why Is Besimple AI Timely?

The timing couldn’t be better.

As AI startups grow and enterprises launch LLM-based tools, the gap between model performance and user expectations continues to widen. Great prompts and infrastructure aren’t enough — it’s the annotated data that makes the difference.

With Besimple, teams can:

Improve LLM quality without waiting weeks for label cycles
Understand why production models are failing
Scale training without scaling human effort proportionally

In a world of rising AI complexity, Besimple brings clarity and speed.

What’s Next for Besimple?

Besimple’s roadmap includes:

Expanding multimodal annotation (image/video + LLM traces)
More sophisticated AI Judges that learn team-specific annotation styles
Plugins for common ML ops stacks and training frameworks
Open benchmarking and leaderboards for annotation consistency

But the core mission remains unchanged: making high-quality, human-reviewed data simple, scalable, and fast.

Who Should Use Besimple AI?

Besimple is ideal for:

AI startups iterating on product-market fit
Enterprises deploying LLMs and needing governance-compliant workflows
Research labs are building new datasets
Teams frustrated by spreadsheets, stale pipelines, and unclear model failures

If your model fails and you don’t know why, Besimple helps you find out and fix it fast.

Final Thoughts: Can Besimple Become the Next Scale AI?

In many ways, Besimple is aiming for a radical democratization of what once took months and millions to build internally. What Scale AI did for static data, Besimple is doing for dynamic, production-grade AI pipelines.

It’s an annotation, not as a service, but as a platform anyone can launch in a minute.

And in that minute, you move from guessing why your AI fails to training it smarter.