The Evolution of AI: Skills Framework for Cross-Model Expertise

Business leaders and IT executives alike are searching for ways to make artificial intelligence more reliable, versatile, and capable of handling specialized tasks with precision. While AI has made tremendous strides in recent years, it has historically struggled with certain limitations—particularly when faced with complex calculations, specialized workflows, or the need to follow detailed technical processes with unwavering accuracy.

With new skills capabilities, AI systems now have access to deploy specialized skillsets and overcome some of these historical limitations.

The evolution of AI capabilities

Recently, Anthropic introduced Claude Skills, allowing their AI assistant to dynamically load expert instructions and executable code when relevant to a task. Vertesia has taken this innovation further, implementing this powerful pattern across every AI model in our ecosystem—creating a universal standard that works consistently regardless of the underlying model.

This universal implementation marks a critical milestone in AI development, addressing one of the core limitations of large language models—their historical struggle with reliably performing specialized tasks. By standardizing how AI assistants access specialized capabilities, Vertesia has positioned itself at the forefront of AI evolution.

How Vertesia's skills transform AI workflow

The skills implementation offers several key advantages that distinguish it from model-specific approaches:

1. Cross-model compatibility: Unlike native skills implementations that are limited to specific models, Vertesia's skills work consistently across all supported AI models.
2. On-demand tool access: Any model can recognize when it needs specialized capabilities and call upon the right tools without explicit user direction.
3. Efficient context management: By loading only what's needed when it's needed, leveraging skills maximizes the AI's effective context window and processing capacity.
4. Composable expertise: Multiple skills can be stacked and coordinated automatically, allowing models to bring together different areas of expertise for complex tasks.
5. Consistent performance: Skills ensure that all models follow the same best practices and workflows consistently across conversations.
6. Code execution when appropriate: For tasks where programmatic solutions are more reliable than natural language generation, skills can include executable code. This capability is a core strength of the platform, designed to complement and leverage other features. For instance, the code execution can feed on prepared content, process specific pages or text parts, or analyze data that the model receives from our knowledge preparation pipeline, acting as a powerful supplement to the natural language outputs.
7. Handy tools: Things like a calculator, to ensure the model is actually calculating and not estimating. 

Practical use cases: Automated financial document analysis & reporting workflow

For businesses dealing with complex financial documentation—such as invoices, receipts, balance sheets, and profit & loss statements—the process of preparation, data extraction, and standardized reporting is often time-consuming, requires specialized data manipulation, and is prone to human error.

The challenge:

• Poor image quality of physical documents (e.g., faded receipts, crumpled invoices) making data extraction unreliable.
• Manual manipulation of raw financial data needed before standard reporting can occur (e.g., calculating subtotals, consolidating entries).
• Inconsistent extraction of key financial figures across various document formats.
• Labor-intensive creation of standardized reports, such as properly formatted Profit & Loss (P&L) statements, that must feed from previously processed financial data.

Vertesia’s solution using skills:

With Vertesia's skills capabilities, an AI assistant can automatically:

• Recognize when it's dealing with financial document analysis or reporting and invoke specialized manipulation and data skills.
• Enhance image clarity (e.g., adjust contrast, straighten) using a specialized image processing skill to make handwritten or poor-quality documents more readable for the data extraction model.
• Extract and classify financial data (e.g., identifying revenue, expenses, liabilities) from documents like invoices and receipts.
• Manipulate and calculate the extracted raw financial data (e.g., calculating depreciation, standardizing chart-of-account codes).
• Create and format a standardized financial report, such as a Profit & Loss (P&L) statement, directly feeding from the previously extracted and manipulated data.

Business impact:

• Improved data accuracy due to clearer source images and standardized processing.
• Significantly reduced time spent on preparing and formatting standardized financial reports.
• Increased reliability of extracted financial figures across varied document types.
• Faster and more consistent reporting for better operational and investment decision-making.

The future of business AI with Vertesia skills

The introduction of universal skills across all AI models represents more than just a technical enhancement—it fundamentally changes what's possible with enterprise AI. By addressing core limitations of large language models and creating standardized extension patterns, Vertesia has eliminated significant barriers to AI adoption in specialized business contexts.

As we look to the future, this approach opens new possibilities for AI-augmented business processes where specialized knowledge, complex calculations, and strict procedural adherence are required. Organizations can now confidently deploy AI assistants for mission-critical functions, knowing they can reliably perform specialized tasks with the precision and consistency the business requires.