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AI that actually works: built for chemical procurement

AI that actually works - built for chemical procurement

Finding chemical and raw material suppliers is tougher than ever. Use supplier scouting technology to quickly uncover relevant, qualified alternatives

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Nabil Azim

<h2>AI and Chemical Procurement</h2>
In the quickly evolving landscape of artificial intelligence, Generative AI and Large Language Models (LLMs) have emerged as transformative (no pun intended 😀) technologies, drawing the interest of both industry professionals and the general public. Models such as Anthropic&#8217;s &#8220;Claude 3.5 Sonnet&#8221; and OpenAI&#8217;s &#8220;o1&#8221; have demonstrated remarkable capabilities in generating human-like text, engaging in complex dialogues, and even assisting with web research. 
At their core, LLMs operate on pattern recognition and prediction. They are trained on vast amounts of text data, including websites, books, and articles, allowing them to learn the statistical relationships between words and phrases. Built on a transformer architecture, LLMs use self-attention mechanisms and unsupervised learning to generate contextually appropriate text. This foundational approach opens opportunities across various domains, but there remains a gap in specialized sectors like chemicals, manufacturing, raw materials, etc.
These are some of the top challenges in applying this technology in chemical &amp; raw material procurement:
<ul>
<li aria-level="1">Reliance on General Knowledge: LLMs are trained on broad, publicly available data, whereas chemical procurement often requires proprietary, constantly changing information.</li>
</ul>
<ul>
<li aria-level="1">Lack of Domain-Specific Reasoning: The ability to generate plausible-sounding text does not equate to the deep, specialized reasoning required for complex procurement decisions.</li>
</ul>
<ul>
<li aria-level="1">Absence of Real-Time Data Integration: Traditional LLMs operate based on their training data, which becomes outdated, and cannot inherently access or process real-time market data, pricing information, or supply chain updates.</li>
</ul>
<ul>
<li aria-level="1">Inability to Handle Structured Data: Procurement often involves processing and generating highly structured data (e.g., material specifications, pricing tables), which is not the strength of text-based LLMs.</li>
</ul>
<ul>
<li style="font-weight: 400;" aria-level="1">Challenges with Numerical Reasoning: Many procurement decisions involve complex numerical analyses, which traditional LLMs struggle to perform accurately.</li>
<li style="font-weight: 400;" aria-level="1">Lack of Explainability: The &#8220;black box&#8221; nature of LLMs makes it difficult to understand or audit their decision-making process.</li>
</ul>
These limitations can lead to potentially costly errors in high-stakes industry environments, like procurement and qualification processes. Even with the most sophisticated models, an &#8220;off-the-shelf&#8221; LLM might suggest, from the results of a web search, a supplier based on outdated information or overlook critical factors such as recent regulatory changes, supply chain disruptions, or specific material grade requirements.
&nbsp;
<h2>The Search for a Solution</h2>
To address these challenges, companies are exploring various AI-driven solutions and enabling traditional industries to use AI technologies more effectively. A few examples of approaches currently under development include:
<ul>
<li style="font-weight: 400;" aria-level="1">Industry-specific fine-tuning of LLMs to enhance their relevance</li>
<li style="font-weight: 400;" aria-level="1">Hybrid models that combine machine learning with traditional analysis techniques</li>
<li style="font-weight: 400;" aria-level="1">Multi-modal AI systems capable of processing both textual and visual elements in documents</li>
</ul>
While these approaches represent steps in the right direction, they often fall short of fully addressing the unique needs of the chemical procurement industry, particularly in terms of real-time data integration, highly specialized decision-making, and the level of accuracy required in this field.
At Valdera, we recognize that a more fundamental paradigm shift is necessary to truly revolutionize chemical procurement through AI.
&nbsp;
<h2>Aligning Incentives &#8211; Training by Experts</h2>
Our approach involves building AI solutions specifically designed for the context of chemical procurement. At our core, we’re building a workflow software to automate the procurement process. The procurement process must be broken down into its constituent parts, such as supplier discovery, qualification, and negotiation, and specialized AI algorithms must be applied individually to each to optimize each step. Only after this, can all components be integrated into a cohesive system, handling the end-to-end procurement processes.
We believe that an approach that takes LLMs and builds upon them and trains them to fit into a specialized industry is exactly what is required.
&nbsp;
<h2>Our Ultimate Goal</h2>
Our ultimate goal at Valdera is to build a value-focused AI system equipped with all the data and capabilities required to drive optimal outcomes in direct  procurement.
Our platform helps build trusted collaboration between buyers and suppliers. Buyers can input their requests with their specific needs, and receive quotes from best-fit suppliers, identified by the matching algorithm. To protect suppliers’ intellectual property, we provide a way for them to share their information completely privately and securely. The user is in complete control of who this information goes to. It is only shared directly with matched and trusted buyers they choose, ensuring confidentiality and precision. This provides buyers with accurate, qualified options with long-term partnership potential. Suppliers, on the other hand, gain the ability to market their products more accurately and relevantly, using AI to target the most suitable buyers. This two-sided approach builds a collaborative ecosystem, facilitating strategic connections that benefit all parties involved.
To ensure success, technology must empower users to achieve these outcomes:
<ul>
<li style="font-weight: 400;" aria-level="1">Personalized recommendations: The platform’s adaptive decision support must continuously learn from user interactions and market changes, refining recommendations to align with each company’s unique procurement history and preferences.</li>
<li style="font-weight: 400;" aria-level="1">Planning with predictive analytics: Predictive analytics use historical data and market trends to forecast future procurement needs and challenges.</li>
<li style="font-weight: 400;" aria-level="1">Sustainable sourcing: Technology is required to facilitate companies reaching their goals with speed. Valdera provides a robust framework to evaluate suppliers based on criteria such as carbon footprint, ethical practices, and social responsibility. </li>
<li style="font-weight: 400;" aria-level="1">Regulatory compliance: The latest regulatory information can be used to automatically flag compliance issues and risks to ensure suppliers meet the necessary standards.  </li>
<li style="font-weight: 400;" aria-level="1">Understand total cost of ownership: Procurement requires the ability to transform disparate data into actionable, comparable insights &#8211; balancing cost, service, quality, reliability, and more. Users need to model various scenarios that balance each factor, ensuring smarter, data-driven decisions that go beyond the initial cost match.</li>
</ul>
Valdera’s AI-driven platform is doubling down on these core value areas – the system matches suppliers with buyers but also adds strategic value to the entire procurement process, fostering collaboration, leveraging advanced analytics, and ensuring sustainability and compliance.