How Domain-Specific GenAI Creates Revenue Breakthroughs

Renowned for its complexity, unpredictability and huge data volumes, supply chains stand to gain immensely from general Genai. However, the advantages are even more pronounced with domain-specific GenAI, tailored to address the unique challenges of the freight, logistics, and supply chain sectors.

Gartner predicts that by 2027, over half of GenAI models used by enterprises will be domain-specific. That's up from 1% today. Since this shift from general to vertical-specific GenAI solutions is gaining momentum, I want to unpack the difference between general GenAI and domain-specific GenAI and how it can impact your bottom line.

Understanding General GenAI Models

General GenAI, popularized by models like ChatGPT, has captured our imaginations with its ability to reason and respond and generate human-like responses across a vast array of topics. Models like Claude and Gemini are trained on industrial quantities of general internet data, equipping them with impressive language and content generation capabilities.

Impressive, yes. But, as anyone who has tried using ChatGPT to solve a complex industry-specific challenge can attest, these models lack the precision and contextual understanding needed for mission-critical business applications. That's why there's domain-specific GenAI.

What are Domain-Specific GenAI Models?

Domain-specific, also known as vertical-specific GenAI refers to models trained on data from a specific industry, such as healthcare or supply chain. Trading breadth for depth, domain-specific GenAI gains expert contextual understanding and more accurate outputs.

Unlike general GenAI (versatile but lacking nuanced comprehension), domain-specific GenAI excels in environments where precision and specialized knowledge are non-negotiable--like supply chains.

Harnessing Domain-Specific GenAI in Freight

Until recently, businesses had only two choices on their GenAI menu:

• Broad-spectrum GenAI models: ChatGPT lacks industry focus
• Pre-trained solutions: Building your own model is resource-heavy

Domain-specific GenAI platforms like Stargo combine the best of both models.

• Trained for industry-specific scenarios using extensive industry data sets
• Flexible pricing models and easy integration offer faster time to value

Why is this important to freight forwarders?

Benefits of Domain-Specific GenAI for Freight Forwarding

You're an international freight forwarder. You're managing thousands of emails, invoices, RFQs, POs, BoLs, etc. Speed is of the essence. But that's when gremlins creep in, causing delays and revenue loss downstream.

Domain-specific GenAI relieves this demand on freight forwarders by automating the extraction, cleansing, enriching and processing of this unstructured data.

Here are five vertical-specific GenAI advantages:

1. Sector-Specific Information: Stargo Large Language Model (SLLM) is pre-trained on +1 million samples from 35,000 real-world emails), enabling a deep contextual understanding of freight, logistics and supply chain data and processes
2. Custom-Built Algorithms: Our models are trained to tackle specific freight forwarding challenges, ranging from rate optimization and dynamic pricing to document handling
3. Faster Time to Value: Vertical-specific GenAI models can be rapidly configured and deployed to solve client use cases
4. Exceptional Precision: StarDox processes data more accurately than manual management and general GenAI models
5. Adaptability: Our scalable models can be rapidly configured and deployed across various use cases and departments

Domain-Specific GenAI: A Revenue Powerhouse

By focusing on the nuanced challenges of freight, logistics and supply chains, domain-specific GenAI can unlock value in ways that general AI cannot.

1. Mitigating Risks

Crucially, vertical-specific GenAI also helps mitigate the risks associated with more generic language models, such as hallucinations or irrelevant responses.

By training on curated, industry-specific data, we can ensure that our outputs are grounded in reality and directly applicable to our customers' most pressing concerns.

We further enhance the accuracy and relevance of our domain-specific GenA with Retrieval-Augmented Generation (RAG). This technique combines generative AI with a retrieval system to source relevant, real-time information from industry-specific documents. RAG ensures our models generate precise, fact-based responses, reducing errors and improving decision-making.

2. Cross-Departmental Integration

Moreover, our domain-specific GenAI solutions are designed to work across multiple departments within a freight forwarding organization. From procurement and sales to operations, compliance, and finance, our tools break down silos and create a more integrated, efficient operation.

3. Handling Complex, Industry-Specific Tasks

One of the most exciting aspects of domain-specific GenAI is its ability to handle complex, industry-specific tasks that general AI models simply can't manage. For instance, our solutions can automate processes that require deep industry knowledge and context, such as GRI/GRD quote updates, FMC filing readiness, and commodity mapping.

4. Future-Forward: Sustainability in Freight Forwarding

Domain-specific GenAI can also embed sustainability across your organization. For instance, Stargo helps freight forwarders find sustainable vendors, compare CO2 emissions for different routes and modes in real-time, and monitor supply chain emissions over time.

5. The Bottom Line: Impact

These improvements translate directly to increased annual revenue. Our customers are achieving an 8% improvement in conversion rates, a 20% boost in productivity, and a 5% increase in margins for our clients. These are not just incremental gains; they represent a fundamental shift in how freight forwarding businesses manage data and processes.

Key Metrics from Stargo's Use Cases

• RFQs: StarDox generates 500 lane RFQs in 40 seconds with 100% data accuracy.
  • Productivity: +20%
  • Margin: +4%
  • Conversion Rate: +8%
• Carrier Buy Prices: Dynamic Derivatives Pricing Engine assesses, compares, and identifies optimal carrier contract rates in real-time.
  • Efficiency: 3 hours → 3.6 seconds
  • Accuracy: 40% → 100%
  • Process Costs: $70 → $2.5
• Customer Price Requests: StarDox extracts, cleans, enriches, and structures customer price requests.
  • Productivity: 60 hours → 3.6 seconds
  • Accuracy: 30% → 100%
  • Efficiency: 12 emails → 1 email

Stargo GenAI: The Future is Vertical-Specific

From automating complex document processing to optimizing procurement and operations, our vertical-specific GenAI models speak the language of freight, logistics and supply chain. This precision mitigates risks and ensures AI outputs are contextually relevant, enhancing decision-making and driving sustainable value.

Lastly, we adhere strictly to GDPR and ISO 27001 standards, providing industry-compliant security controls and data privacy protection for your valuable information. Are you ahead of the curve and leaning into domain-specific GenAI? Let's connect and explore the possibilities.