LLM Evaluation Made Simple: Meet The LLM Data Company

The LLM Data Company (TLDC), founded in 2025 and based in San Francisco, is on a mission to bring structure, clarity, and control to one of the most complex challenges in machine learning: evaluating language models and defining reward systems for reinforcement learning (RL). The team—Gavin Bains, Joseph Besgen, and Daanish Khazi—has launched a product suite aimed at solving a problem that’s quietly undermining the progress of generative AI.

In a world flooded with large language models (LLMs), choosing the right one or optimizing your own is no longer just about benchmarking speed or token count. Evaluating model output quality across nuanced tasks, understanding performance trade-offs, and post-training custom models requires more than vanilla tools or manual reviews. TLDC steps in here, offering a suite of tools that let teams write, version, and execute evaluations (evals) and define granular RL reward structures, giving them the confidence to iterate intelligently.

Why Are Evals So Critical in AI Development?

Evaluations—or “evals”—are how teams measure model performance on custom tasks. These aren’t just leaderboards or academic benchmarks; they are tailored assessments that reflect the goals, tone, and priorities of a specific product or user experience.

But designing high-signal evals is notoriously difficult. Vanilla LLM-based judging tends to be inconsistent, lacking the granularity or context-awareness required for complex comparisons. What’s more, most teams still rely on brittle, manual systems to run evaluations, leading to missed edge cases, unclear conclusions, and wasted compute cycles.

Reinforcement learning (especially GRPO-style techniques) is now a common strategy for improving LLM performance post-training. But here’s the catch: it requires precise, scalable evaluations to define the “rewards” that guide the model’s learning process. Without a high-quality signal from evals, the RL process is like trying to teach in the dark.

TLDC is tackling this challenge head-on, introducing a structured, repeatable, and scalable system to take evals from art to science.

What Makes TLDC’s Approach Different?

At the heart of TLDC is doteval, a powerful workspace purpose-built for the modern LLM lifecycle. The experience is akin to using a developer IDE like Cursor, but for evaluation design. In doteval, users write evals-as-code using a structured YAML schema, version them like any other software artifact, and run comparisons across checkpoints—all while integrating automated grading logic and human-aligned rubrics.

Rather than writing scattered notes in spreadsheets or fighting with APIs, doteval users can:

• Write structured eval specs for different task types (reasoning, summarization, code gen, etc.).
• Track changes and iterations using diff views, just like in Git.
• Automatically generate grading diffs powered by AI to replace manual scoring.
• Run side-by-side evaluations to compare performance across different models, versions, or prompts.
• Export successful specs as RL training rewards to improve models in a tightly-looped, feedback-driven way.

The result? Teams spend less time debating prompt tweaks and more time shipping robust, aligned LLMs.

How Does TLDC Enable Smarter RL Training?

Post-training model optimization using Reinforcement Learning with Human Feedback (RLHF) or its generalized cousin, GRPO (Gradient-based Reinforcement Preference Optimization), is becoming table stakes for competitive LLMs. But while frameworks for applying RL exist, defining the reward function—what you actually want the model to do—is still a major blocker.

TLDC solves this by letting teams use high-fidelity evals as reward signals. If your eval rubric captures what a good summary, answer, or analysis looks like, then that same rubric can be used to train your model to reproduce that quality automatically. It closes the loop between evaluation and improvement, turning qualitative insights into actionable learning.

Instead of hiring an army of human annotators or accepting noisy outputs, teams using TLDC can simply define aligned rubrics once and reuse them across training runs, model generations, and downstream assessments.

Who Should Use The LLM Data Company’s Tools?

The TLDC workspace is ideal for any team building, fine-tuning, or operating LLMs at scale. Whether you’re:

• A research team testing the impact of architectural changes;
• A product org deciding whether Claude 4 or Gemini 2.5 better suits your assistant;
• An AI startup building a custom model for a regulated industry;
• Or an open-source contributor evaluating outputs of different fine-tuning approaches…

You’ll benefit from having a centralized, version-controlled, and automatable system to run your evaluations and train off the results.

In a market that increasingly depends on explainability and performance certainty, TLDC provides the infrastructure to confidently say “yes” or “no” to model changes, upgrades, or rollbacks.

What Is the Impact of Versioned Evals on the AI Ecosystem?

One of TLDC’s most powerful innovations is the idea of versioned evals. Just like codebases evolve, so too must the criteria by which models are judged. Versioning evals across model updates and prompt iterations ensures reproducibility and transparency. It also allows AI practitioners to compare current and historical performance under consistent conditions.

This is critical for organizations managing multiple models, working across regulatory domains, or contributing to open benchmarks. With versioned evals:

• Teams gain auditability.
• Researchers can isolate performance regressions.
• Product leads can defend model decisions with evidence.

In short, it brings maturity and professionalism to an area of AI that has long lacked both.

What’s Next for The LLM Data Company?

As LLMs become ubiquitous across industries—from customer support to education to enterprise automation—the demand for better, clearer model evaluation will only intensify. The LLM Data Company is well-positioned to become the “Jira + GitHub” of model evals: the essential operating layer that sits between development, training, and deployment.

In the future, expect TLDC to expand beyond evals to broader data tooling for agents, model monitoring, and automated dataset curation. Its infrastructure-first mindset, developer-friendly interface, and deep understanding of the real pain points in LLM workflows suggest it won’t just stay ahead of the curve—it’ll define it.

Why Does This Matter for the Future of AI?

The progress of AI hinges not just on training bigger models or feeding them more data, but on measuring what matters—consistently, rigorously, and contextually. The LLM Data Company’s platform represents a new chapter in AI infrastructure: one where teams no longer fumble in the dark with qualitative guesswork, but instead build on measurable performance foundations.

By turning evaluation into a core software discipline—complete with version control, automation, and human alignment—TLDC ensures that the next generation of AI models is not just more powerful, but more useful, ethical, and reliable.