Ahmed Mahmoud, CEO of DXwand, shares how the company’s AI agents platform, ORXTRA, is driving real business value.
How does DXwand differentiate itself from other conversational AI providers?
At DXwand, we differentiate ourselves from other conversational AI providers through our unique AI agents platform, ORXTRA. What makes ORXTRA special is that it offers both ready-made AI agents for specific use cases and a builder platform for creating fully customized solutions.
Customization, cost optimization, and transparency are at the heart of ORXTRA. With it, businesses can build AI agents within hours or days by automatically selecting the most suitable combination of large language models (LLMs) based on accuracy and cost-efficiency. In fact, we’ve seen deployments reduce AI costs by up to 90%.
Trust and risk management are equally important to us. That’s why ORXTRA includes built-in explainability metrics—a field where we’ve led innovation, filing 10 patents related to AI tuning and transparency.
One of our proudest differentiators is our advanced mastery of Arabic dialects. Our AI models understand and interact in a wide range of regional variations, which is absolutely critical in markets where language nuance matters.
Beyond conversation, we designed DXwand’s AI agents to be fully autonomous, capable of taking over entire business processes—not just assisting with isolated tasks. This drives real operational efficiency and business value for our clients.
We also built in automated knowledge mining, allowing our platform to extract and label data from documents without any manual input. It’s another way we help streamline workflows and boost productivity.
Lastly, our omnichannel integration ensures seamless experiences across call centers, WhatsApp, Messenger, SMS, and websites—wherever customers need to connect.
With all these capabilities, we believe DXwand truly stands out in the conversational AI space—delivering scalable, transparent, and cost-effective AI solutions tailored to both regional and business-specific needs.
What advancements in NLP and machine learning power DXwand’s solutions?
DXwand leverages several cutting-edge advancements in Natural Language Processing (NLP) and machine learning to enhance its conversational AI solutions:
- Integration of Large Language Models (LLMs):
DXwand utilizes LLMs to generate human-like responses, significantly improving the quality and coherence of interactions. - Integration of Small Language Models (SLMs):
Prism, DXwand’s platform, is designed for accuracy and efficiency. It uses highly optimized hardware to deliver precise, contextually relevant answers in both English and Arabic. This optimization reduces hardware and GPU requirements by over 85% without sacrificing quality. - Advanced Natural Language Understanding (NLU):
Sophisticated NLU techniques allow for a deeper comprehension of user intent, enabling more effective and meaningful conversations. - Explainable AI Mechanisms:
DXwand incorporates explainable AI to ensure transparency in decision-making—a critical factor for trust and compliance, particularly in sectors like healthcare and finance. - Automated Knowledge Mining:
The platform automates the extraction and labeling of data from documents, enhancing onboarding and operational efficiency for enterprises.
How does DXwand handle multi-language and dialect support?
At DXwand, we take multi-language and dialect support seriously—especially when it comes to Arabic. We enhance this capability by developing custom AI models, acquiring real-world data, and generating synthetic data to fill critical gaps. To ensure the highest level of accuracy, we build industry-specific AI models and also leverage client-provided datasets to fine-tune our understanding of multiple Arabic dialects.
We’ve made significant strides by fine-tuning LLaMA 3.2 models, boosting Arabic language benchmarks from 20% to an impressive 70%. Our Prism models give clients flexibility with two options: a 1B model for fast, cost-efficient performance, and a 3B model for more complex queries requiring higher accuracy. With this approach, we ensure that our multilingual AI solutions are scalable, efficient, and capable of delivering high performance across a wide range of use cases.
How does DXwand ensure the accuracy and contextual understanding of its conversational AI models?
We prioritize responsible AI through assurance, explainability, and live monitoring. ORXTRA has integrated these from the start, evolving over time. Half of DXwand’s patents focus on accuracy and transparency, using rigorous benchmarking to align test results with real-world performance.
The system generates diverse test questions to validate accuracy across all content areas, ensuring reliable AI outputs. It also evaluates responses for completeness and relevance while enforcing compliance checks for safety, bias, and accuracy. This commitment helps prevent failures post-launch and ensures trustworthy AI solutions.
How do evolving regulations, such as the EU AI Act, impact the deployment of conversational AI?
Evolving regulations like the EU AI Act impact conversational AI by enforcing stricter compliance, transparency, and ethical standards. AI systems are categorized by risk levels, with high-risk applications facing greater scrutiny.
Regulations mandate disclosure when AI is used, explainability of decisions, stronger data privacy protections, and bias mitigation to ensure fairness. Non-compliance can result in significant fines, requiring businesses to continuously monitor and adapt AI models. These regulations drive AI towards greater security, fairness, and accountability, ensuring responsible deployment while balancing innovation.
What advice would you give businesses that are just starting their conversational AI journey?
My biggest advice: don’t over-plan—focus on execution. Many businesses get stuck in endless strategizing, trying to perfect their AI approach before even getting started. Instead, take action. Start small, test in real-world scenarios, and learn as you go.
The process itself will reveal what works and what doesn’t, allowing you to refine your strategy based on actual user interactions. The key is iteration over perfection—invest in continuous improvement rather than waiting for the “perfect” plan.
