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Our take on DeepSeek

Artificial Intelligence is evolving at an unprecedented pace reshaping industries and redefining possibilities all the time. At LexX, we are at the forefront of this transformation, harnessing next-generation foundational models to tackle real-world challenges. Our flexibility allows us to explore new AI methods, architectures and models—like DeepSeek—that offer a compelling alternative to traditional large-scale models. But beyond the shock and awe factor, that DeepSeek, seems to have generated, how they solve key industry and domain pain points is key to why enterprise customers should consider DeepSeek.

From a LexX standpoint foundational models like DeepSeek or Gemini or OpenAI shouldn’t matter, when the opportunity really lies in building applications that solve specific customer problems. In that sense we would welcome all foundational models. Let’s explore a bit more.

Challenges in Using Large-Scale AI Models

While large AI models bring unparalleled capabilities, they also come with significant challenges:

🔹 Infrastructure Demands: Running large-scale AI requires immense computational power, making deployment costly and resource-intensive.

🔹 Latency Issues: Real-time applications struggle with performance lags due to the sheer size of traditional models.

🔹 Cost of Scalability: Expanding AI-powered solutions across multiple environments can lead to skyrocketing operational expenses.

🔹 Limited Accessibility: AI adoption in edge computing environments, such as remote or isolated systems, remains a major hurdle.

As businesses seek scalable, cost-effective, and high-performance AI solutions, the need for a new approach is clear. This is where models like DeepSeek become relevant. Although still to be tested for safety in enterprise applications, we believe as a model capable of being deployed on local hosts, DeepSeek will challenge many existing models as a cost effective solution.

Where DeepSeek fits in. The Benefits

Innovative AI models are bridging the gap between power and efficiency, offering a host of advantages:

🔹 Efficiency Without Compromise: Optimized architecture ensures fast inference speeds while maintaining accuracy in domain-specific tasks.

🔹 Edge Compatibility: Designed for resource-constrained environments, enabling real-time AI capabilities in remote locations.

🔹 Cost-Effectiveness: Lower infrastructure requirements translate to scalable AI adoption without excessive overhead.

🔹 Fine-Tuning Flexibility: These models can be adapted to specific industries, ensuring high accuracy in our applications like xAssist and xIntelisearch and other mission-critical use-cases.

LexX’s Platform Approach

At LexX, we take a platform approach to AI, to operate at the application layer, to solve specific customer use cases rather than focusing on training foundational models. By combining best-in-class foundational models within a modular, scalable framework a platform approach also offers standardised interfaces to ingest data, standardised capabilities to configure our applications for domain specific use cases and the ability to isolate models completely, if required, from the internet and restrict access to a local host or network. Our strategy includes:

🔹 Hybrid Model Strategy: We deploy efficient foundational models if it makes sense for our application use cases, alongside larger systems to ensure optimal performance across workflows. This means that as new models emerge we evaluate them for potential application to our industry focus, and can incorporate them into the platform seamlessly and use them side by side with other models, systems and processes if required.

🔹 Data Optimization: Our AI models leverage high-quality, preprocessed data to enhance adaptability and performance. Without data optimisation that our platforms allow for, foundational models alone target won’t target or solve critical business challenges.

🔹 Edge Deployment: We enable AI capabilities within isolated environments by using lightweight models for localised applications yet benefitting from globalised training. A platform approach facilitates this requirement to isolate operations and data exposure, should the business application require it as in defence applications.

🔹 Seamless API Integration: A robust API architecture ensures smooth interoperability, facilitating efficient workflows across our AI platform ecosystem. The creators of AI models does not always design for the complexity of legacy environments that may require a tailored integration approach.

Join the conversation!

Comment below or reach out to us to discover how our AI strategies can transform your business. At LexX, we aim to strike the perfect balance between power, efficiency, and scalability. If you’re exploring AI for mission-critical enterprise applications, let’s collaborate and unlock new frontiers in AI-driven innovation with or without DeepSeek.

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