Key takeaways

• High-quality training data is essential to enterprise AI. The quality of your AI responses depends on the quality of your dataset.
• Traditional data prep requires a lot of time, money, and resources. This presents a sizable hurdle that can slow down or halt AI initiatives.
• Even after heavy investment, manual data prep can face quality issues. Irrelevant, erroneous, or disorganized information can lead to inconsistencies in model outputs and potentially cause hallucinations.
• Synthetic data generation streamlines your AI model training. The SeekrFlow™ platform allows you to generate an ideal training dataset by simply uploading relevant information and stating your AI use case.

How training data sets the stage for enterprise AI results

Gartner recently reported that through 2025, a minimum of 30% of generative AI initiatives will be abandoned after proof of concept—with poor data quality being one of the primary factors contributing to project termination. Depending on your experience in AI, you may not find that statistic surprising. Many enterprise leaders are discovering that training datasets, and how one goes about obtaining them, plays a pivotal role in AI model accuracy and overall program success. Still, a recent Monte Carlo survey shows that 68% of data leaders lack confidence in their data quality. The same survey revealed that, in the last six months, two-thirds of organizations surveyed had experienced data quality incidents costing $100,000 or more.

Factor it all together and it’s easy to see why data acquisition, preparation, and generation are significant areas of concern for enterprises. But many find themselves needing to progress AI development even when they know their data isn’t ideal. This can cause serious problems during deployment, such as:

Hallucinations and inaccuracies: Bad information in your training data can lead AI to generate erroneous or irrelevant responses. AI accuracy is closely tied to the training dataset.

Trust and AI compliance issues: If personally identifiable information (PII) or other types of sensitive data are included in the training dataset, it can fail to meet data privacy laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

Inflated costs: Misaligned datasets can lead to longer training times to reach convergence as well as wasted compute time during training.

Given this sizable stack of challenges and the high stakes, many enterprise teams find themselves wondering, “Is there an easier way to get the data my use case needs?”

The quick answer is yes. Synthetic data generation capabilities—such as those available in SeekrFlow’s Principle Alignment feature—are solving training data challenges for enterprises. But before we explore how you can stay ahead of the curve, let’s take a closer look at the challenges of traditional data preparation.

The problem with traditional data preparation

The first step of a typical enterprise AI pipeline involves data acquisition. Here, enterprises find data sources that can teach your model what it needs to know. Sometimes they have this data already, but it can also be purchased from third-party data providers. Many enterprises, especially those pursuing highly specific and nuanced use cases, can struggle to even find the type of data they need—let alone supply it at volume. This lack of training data can stop many enterprise AI initiatives before they deliver ROI.

For those who find the data they need, there’s still plenty of work to do. After gathering, data needs to be preprocessed, structured, and annotated. This is a lengthy and time-consuming process. According to Hackernoon, cleaning and removing errors from 100,000 samples can take 80 to 160 hours—and annotation of 100,000 samples for supervised learning models can require 300 to 850 hours.

AI model training optimization through agentic dataset generation

At Seekr, we’re leveraging an agentic data generation workflow to help enterprises avoid the headaches of manual data preparation. With our SeekrFlow platform, you can autonomously generate data—part of a feature we call Principle Alignment—to achieve optimized AI model training.

When you use Principle Alignment, moving from a limited or nonexistent dataset to deployment is easy:

1. Tell our system your goal. This could be something like, “I’m building a chatbot to answer employee questions about our internal policies.”

2. Upload one or more relevant documents. These could be anything that contains data that’s essential to your use case. For example, a company handbook.

3. Generate your ideal training dataset. SeekrFlow combines your goal and your inputted information to autonomously create the data your use case requires.

4. Customize a large language model (LLM). SeekrFlow puts your training data to work to teach an LLM how to accomplish your goal.

5. Validate, optimize, and deploy. SeekrFlow is a one-stop shop that spans the entire AI lifecycle.

Through autonomous generation, SeekrFlow can help you meet your data needs 2.5x faster than manual processes—while enabling 3x more accurate model responses compared to traditional approaches.

Ultimately, organizations that use SeekrFlow can arrive at the accurate model performance they need while spending 9x less than those that use traditional methods.

To see the platform in action—including how we incorporate common AI optimization techniques such hyperparameter tuning and transfer learning—check out this brief explainer video.

Test and validate to maximize AI model training optimization

Testing and validation are also critical aspects of AI model training optimization. To help enterprises further refine their model’s performance, SeekrFlow provides rich explainability features. These enable developers to understand, contextualize, and improve model outputs, leading to more reliable AI solutions.

SeekrFlow explainability and contestability features include:

• A side-by-side comparison view that shows how two models respond to the same query, allowing developers to choose the more accurate model for their use case
• Contestability tools to help developers challenge and refine model outputs

Go from data to deployment faster with high-quality synthetic data

Training data is a critical consideration for any enterprise pursuing AI use cases. Since an AI model’s accuracy is only as strong as the data it’s built on, enterprises must implement a well-thought-out data management strategy to succeed. With SeekrFlow, you can generate the dataset you need in hours instead of months or years—alongside testing and validation tools to help further optimize model accuracy.

No matter where you are on the road from concept to deployment, we can help you navigate the complex world of enterprise AI and simplify your path to ROI. Find out more about the SeekrFlow AI platform today.