This is the ‘no-code’ collaborative data solution CROs, product leads, and other non-developers have been waiting for.
While artificial intelligence has democratized a wide range of creative tools for data and analytics, one key area has been left behind: AI model training. Narrative has been steadily bridging this gap.
Today, we're thrilled to unveil a game-changing breakthrough in AI model training. This innovation empowers everyone—from chief revenue officers to product development teams—to train AI models, monetize data, and collaborate effortlessly with clients and partners on AI projects—without the need for complex coding, configuration, or infrastructure.
In simple terms, Narrative’s latest release transforms raw, disparate datasets into valuable training material using an easy point-and-click interface and robust backend technology.
The landscape of AI model development is about to change dramatically with Model Studio and Rosetta Stone 2.0.
Model Studio, an intuitive point-and-click training tool, and Rosetta Stone 2.0, our advanced data normalization solution, address two critical challenges in AI development:
- customizing AI models for specific business needs
- making diverse data sources work together seamlessly
Understanding AI Model Training
Let's start with what model training means in practical terms. Think of a large language model (LLM) as a highly capable but general-purpose AI system. While it might understand English well, it may not grasp your industry's specific terminology or your organization's unique ways of doing things. That’s where fine-tuning takes over.
Model training, or more specifically fine-tuning, is the process of teaching the model specifics. These may include examples such as this:
- A healthcare provider training the model to understand medical terminology and standard procedures
- A financial institution training it to interpret complex financial documents and regulatory filings
- A manufacturing company training it to understand technical specifications and industry standards
How Narrative Democratizes Model Training
Today, this kind of customization requires advanced ML engineering resources. The painstaking work of building, validating, or sourcing relevant training datasets is another enormous demand on a company’s various tech, marketing, and sales teams.
Model Studio and Rosetta Stone 2.0 turns this into a task anyone can do — literally.
Narrative’s intuitive interface and AI-powered backend simplify complex processes, handling all the technical details. Plus, the platform offers easy access to a marketplace of datasets for training your models.
This ensures that data from various sources is converted into a consistent, standardized format that works seamlessly in the background—like a universal translator for your data.
How Model Studio opens the doors for anyone to do AI development:
- Point & click workflow allowing business users to train models and not just data scientists
- Training data is automatically normalized by Rosetta Stone
- Easy sourcing of proprietary data via Narrative marketplace to expand data beyond your owned assets and augment Training
- A set of libraries allow fine-tuned models to operate not just as a chat bot, but as a systems expert that can interact with your enterprise’s technology stack
Consider these real-world scenarios that Rosetta Stone 2.0 handles:
- Sharing data with partners that each store data in a different way, for example timestamps.
- Map between standards like country names and country codes
- Help normalize data from internal teams with different data capture methodologies, ex: managers of retails stores who each report sales and inventory data slightly differently.
Unlike traditional tools that require manual mapping for each new data format, Rosetta Stone 2.0 can intelligently recognize and standardize any new data it encounters. It is, essentially, a super-powered data analyst instantly understanding and standardizing any new dataset you provide.
For technical teams who've wrestled with these challenges, Narrative represents a significant breakthrough. The platform can work with diverse data sources while maintaining precise control over data usage. Organizations can now fine-tune models using combinations of proprietary data, partner datasets, and marketplace content – all without writing code.
Rosetta Stone 2.0: Beyond Basic Data Normalization
Consider a manufacturing company dealing with supplier data from different countries. Traditional tools would require manual mapping for each supplier's format. Rosetta Stone 2.0 can automatically understand that "Part_No", "ItemID," and "Component_Number" all refer to the same thing, even if it hasn't seen these specific terms before.
Rosetta Stone 2.0’s standout features:
- An advanced intelligent system that is more frequently updated to keep up with the rapid and constant tech progress that too often makes solutions quickly obsolete and outmoded. There’s no more “catch-up” needed by users of Rosetta Stone 2.0.
- Multiple model sizes to adapt to the speed and precision levels required by your organization (3B, 8B, 70B parameters respectively).
- Most importantly, you can train Rosetta Stone 2.0 on your own data and optionally your own custom data attributes — all without exposing your data back to Narrative.
The system's ability to learn from organizational context is particularly powerful. Users can fine-tune the normalization model to understand company-specific terms or industry-specific standards.
For instance, a global retailer could train the model to normalize product categories across different regional naming conventions without requiring manual mapping.
A New Approach to Data Monetization
Perhaps the most notable aspect of this release is our reimagining of data monetization. Organizations can now profit from their data in two distinct ways:
- Offering datasets specifically for training other organizations' AI models
- Monetizing their pre-trained models while keeping their underlying data private
This creates interesting opportunities. A healthcare provider, for instance, could monetize their anonymized medical records specifically for training diagnostic models without exposing the raw patient data. The platform's granular access controls ensure that data providers maintain precise control over how their information is used.
A Huge Step Forward
From an infrastructure perspective, Narrative eliminates many common pain points. Organizations no longer need to maintain complex systems for data cleaning and standardization, as these functions are handled natively within the platform. This not only reduces technical overhead but also simplifies compliance and security management.
Our new model training and Rosetta Stone 2.0 represent a significant step forward in making AI development more accessible and practical. By combining easy-to-use model training tools with sophisticated data normalization and a flexible monetization framework, they've created a solution that addresses the real-world challenges of building and deploying custom AI solutions.
For technical leaders looking to accelerate their AI initiatives while maintaining control over data and models, Narrative offers a compelling alternative to traditional development approaches. The ability to leverage diverse data sources while maintaining granular control over data usage could fundamentally change how organizations approach AI development.
Model Studio and Rosetta Stone 2.0 ready for immediate deployment. Organizations interested in either custom model development or data monetization can begin exploring these capabilities without the traditional overhead of building out specialized infrastructure or data science teams.
Get in touch to learn more!