Optimized LLM data that outperforms pure human data.

Yes, when done right. In the past, many companies found fine-tuning wasn't worth the effort due to expensive data preparation. Data makes or breaks performance, so when data preparation falters, the model performance suffers.

However, with proper training data, fine-tuning now delivers clear benefits:

• Happier users who get more accurate and relevant outputs

• A competitive edge with AI that truly understands your business

• Lower costs in the long run (less prompt engineering, more efficiency)

This matters more than ever with AI agents, where accuracy is critical. Consider this: if your model is 70% accurate on individual tasks, it might only succeed 10% of the time when completing multi-step processes.

The constant release of new base models doesn't reduce the need for fine-tuning—it reinforces it. Base models will never be trained specifically on your company context, products, or unique business rules. The data assets you develop for fine-tuning become a lasting competitive advantage that you can apply to each new model generation.

With our platform's approach, your fine-tuning investments become increasingly valuable over time:

• Reuse your LLM-ready data assets to quickly adapt any new base model

• Run experiments to build new AI features without starting from scratch

• Build proprietary AI capabilities only your company can deliver by combining cutting-edge models with your unique data, domain expertise, and business context

Our platform makes fine-tuning predictable and measurable instead of an expensive guessing game with a research-backed systematic approach - rather than costly trial and error, we provide a structured methodology that delivers predictable improvements and measurable ROI.

When AI models are trained on synthetic data that hasn't been properly checked or lacks variety, your model can start giving repetitive, limited answers instead of the diverse, creative responses you need. Your model can also start hallucinating uncontrollably. Think of it as feeding your AI a limited diet over and over - eventually, it forgets what other foods taste like.

Our platform helps in two critical ways:

1. Diverse Generation: We developed proprietary diverse synthetic data generation methods for LLM post-training that can be customized to your business rules. 

2. Strategic Data Curation: We build custom verification tools that automatically check your synthetic data at scale, filtering out low-quality examples before they damage your model. We ensure only high-quality, diverse synthetic data makes it into training, preserving your model's ability to generate varied, relevant responses.

Synthetic data can dramatically improve AI performance, but without proper safeguards, you risk training on irrelevant or nonsensical examples that violate your business rules. Our platform makes advanced data preparation techniques from leading AI research labs accessible through simple natural language instructions. With our approach, you get all the benefits of synthetic data without the risks that typically make fine-tuning a gamble for businesses.

Our data-first approach maximizes your proprietary data's value through our proven process:

1. Custom Benchmark: We work with domain experts (either from our network or your company) to build a benchmark that accurately reflects your production environment. This benchmark becomes your performance baseline and identifies gaps in your current capabilities in serving your users effectively. We strategically use synthetic data to identify and create edge cases your real data might miss, ensuring comprehensive coverage of your use cases.

2. Targeted Training Dataset Development: Based on the benchmark results, we help you create an optimized training dataset using our efficient data preparation technology. Real data is often noisy. Our tools transform messy data into high-performance training data that models can best learn from, cutting your development time and costs.

3. Performance Validation: After training, we evaluate your model against the custom benchmark to measure tangible improvements.

The right data performs much better than more, unoptimized data. Our systematic approach eliminates guesswork and transforms your proprietary data into your greatest competitive advantage.

We've worked with clients who had no production data pre-launch. We've seen customers who needed a reliable model before they could launch their product, but had nothing to train on. Here's what worked:

1. Limited expert labeling: We collaborated with their domain experts to manually label just 50 high-quality examples representing their core use cases.

2. Synthetic expansion: From those 50 examples, we generated and verified a larger training dataset using synthetic data techniques, focusing on maintaining the quality standards established by the expert-labeled examples. 

3. Post-launch improvement: After launching with this initial model, the client collected real user interactions, which we incorporated into both the benchmark and training data for continuous improvement on observed user behavior.

Another case was already having a product in market with production data. In this case, with our tooling, you could analyze actual user behavior and identify underperforming areas, and generate synthetic data to close those gaps.

The key insight from these cases is that even a small amount of high-quality labeled data with clear annotation rules (50-100 examples) can provide enough signal to bootstrap a functional model using synthetic data techniques, when launching with zero real-world data. Read about a relevant case study here.

We offer a no-risk pilot: a focused assessment and proof of concept with measurable performance gains.

Our team can work with you to assess your specific requirements, analyzing where current models fall short for your use case. We would then build tailored benchmarks using your data to mimic what your model would experience in production, to ensure measured model improvement will be felt by your users.

We demonstrate value through a single data generation and fine-tuning cycle, showing measurable performance improvements on your custom test sets. You'll see clear metrics showing the performance delta achieved with our tooling compared to baseline models, de-risking our methods on your use case.

Our methodology has proven effective across diverse domains from hardware design to healthcare to voice dictation. The same fundamental principles drive model improvement regardless of domain - we identify failure patterns and create precisely engineered data to address them, consistently achieving superior results with dramatically less data than conventional approaches. After demonstrating results, we provide complete knowledge transfer so you can apply these same methods to any model in your workflow via the platform.

Our platform can be used by AI researchers to non-technical product teams. If you can define what your product/model needs to do, you can create the data needed for it. Our team can work with you to develop the evaluation framework and ensure the best LLM development practices.