As artificial intelligence continues to reshape industries, the challenge for many organizations lies in making advanced technology both practical and scalable. DXwand, an AI-focused platform company, is tackling that problem head-on by offering solutions that are not only powerful but also cost-effective and easy to deploy. Through its ORXTRA platform, DXwand is helping businesses automate complex workflows, improve decision-making, and scale operations with minimal friction.

In this interview, the DXwand team shares insights into what makes their approach to AI stand out, how their proprietary mix of language models and knowledge graphs power real-world applications, and why flexibility is central to their product philosophy. From redefining customer support and procurement processes to launching AI-powered educational assistants like Kuwait’s “Chat with Hamad,” DXwand is making a strong case for AI that adapts to local contexts and delivers tangible results.

Below, we dive into the company’s approach to generative AI, the balance between large and small language models, and what the future might hold for intelligent agents in business and education.

Q&A with DXwand

1. Can you introduce DXwand to our readers and tell us what sets your approach to AI apart from others?
At DXwand, we’re all about making AI accessible and effective for businesses of all sizes. Our platform, ORXTRA, offers a range of pre-built AI agents tailored for specific use cases, as well as a builder tool for creating custom solutions. What really sets us apart is how we prioritize cost-efficiency, accuracy, time to market, transparency, and flexibility.
For example, ORXTRA lets businesses build AI agents quickly—sometimes in just a few hours or days. It automatically picks the best combinations of language models to ensure both accuracy and cost-effectiveness, reducing deployment costs by up to 90%. Plus, we provide clear metrics to help companies trust and manage their AI systems. We’ve even filed 10 patents to protect our innovations.
Another key differentiator? We’ve got a deep understanding of Arabic dialects, so our AI works seamlessly across different varieties of Arabic. We also focus on making AI as autonomous as possible—our agents can take over entire business processes. Add in automated knowledge mining, omnichannel integration, and a commitment to transparency, and you’ve got a really unique approach to AI.

2. Generative AI models have exploded in capability recently. How does DXwand leverage large language models (LLMs), knowledge graphs, or other AI techniques under the hood?
We’re really excited about the power of AI and the technologies we use to make it smarter. At DXwand, we leverage a mix of large language models (LLMs), knowledge graphs, and machine learning to create AI agents that not only understand language but can also make intelligent decisions.
LLMs help with natural language understanding and generating responses that feel human-like. Knowledge graphs let us map out relationships and entities, making search and information retrieval more accurate. On top of that, we use agentic platform to really fine-tune how our AI interacts with users. These techniques come together to provide highly personalized and efficient solutions across various industries.

3. DXwand offers both large and small language models within your AI solutions. Can you break down the key differences between the two, and explain which model types are better suited for specific industries or use cases?
The choice between large and small language models really comes down to your specific needs.
Large Language Models (LLMs) are amazing when you need depth, creativity, and nuanced conversations. They can handle complex queries and generate sophisticated, human-like responses. However, they require a lot of computational power, so they’re best for industries like education, healthcare, customer service, and legal support, where rich, context-aware conversations are key.
On the other hand, Small Language Models (SLMs) are optimized for speed and efficiency. They’re much lighter on resources—up to 85% less GPU usage—so they work really well for industries that need fast, precise answers, like banking, telecom, retail, or HR support. SLMs are all about operational efficiency and low-latency responses, which is why they’re great for high-volume environments.
So, if you need conversational depth and advanced reasoning, go for LLMs. But if you need speed and cost-effectiveness, SLMs are the way to go. If you ever wish to have the best of two worlds, we can help fine-tune Prism, our SLM, to give some sophisticated reasoning with still lighter GPU resources.

4. What inspired the partnership with Kuwait’s Ministry of Education, and how did the vision for “Chat with Hamad” come to life?
The partnership with Kuwait’s Ministry of Education came from the need to provide real-time, personalized educational support to students and teachers without overwhelming the staff. They wanted a digital, AI-powered solution that could help everyone from students to parents, in line with the national curriculum.
That’s where our ORXTRA platform came in. We integrated it with Kuwait’s national curriculum to offer a 24/7 multilingual support system. “Chat with Hamad” was born out of this collaboration, providing a scalable and efficient way to support educational needs. It was all about making education more accessible while reducing the workload on human staff, and the AI system launched quickly and seamlessly to meet those goals.

5. How is DXwand helping businesses break barriers with AI, and what are some of the biggest benefits companies are seeing today?
We help businesses embrace AI in a way that’s both simple and impactful. Our platform is designed to get you up and running quickly, without the need for extensive training. This allows businesses to start seeing results fast and scale their solutions as they grow.
One of our biggest successes has been in procurement. We’ve seen huge benefits in this area, with many of our campaigns focusing on optimizing procurement processes. The AI agent we’ve developed for this has helped businesses save costs, make faster decisions, and improve efficiency.
But we’re not just about one use case. We work across multiple areas—like customer support, HR, and supply chain—helping businesses streamline their operations and improve their bottom line. Ultimately, our goal is to make AI accessible, flexible, and capable of scaling across different business needs.

6. What are your future plans for DXwand, and how do you see AI agents evolving in the coming years?
We’ve got some exciting plans ahead! We’re actively forming strategic partnerships and positioning ourselves as a key player in the AI space. While we’ve had acquisition offers in the past, we’re focused on building something that aligns with our vision and valuation.
Looking forward, AI agents will continue to evolve, becoming more autonomous and emotionally intelligent. They’ll be better at understanding human interaction and will make decisions more independently. We also see a future where AI seamlessly integrates across devices and provides a connected, personalized experience for users. As we move forward, we’ll also be addressing ethical concerns to ensure fairness and collaboration with humans.
While we’re still a bit away from true general AI, the agents of the near future will be much more adaptable, handling a broader range of tasks and becoming more efficient as they learn from each interaction.