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Perspective
AI is transforming business operations beyond chatbots and search engines. Emerging AI architectures are creating faster, more tailored, smarter, more efficient, and scalable solutions.
At Trideca, we’re at the forefront of designing and helping organisations understand and adopt the latest AI architectures to gain a competitive edge. We have built a world-leading AI Engineering and delivery practice that can help you understand and leverage these innovations, ensuring you’re always ahead of the competition. Here’s a quick dive into some of the most exciting AI architectures that we are seeing reshaping the business and technology landscape:
Mixture of Experts (MoE): Tailoring Expertise to the Task
Mixture of Experts (MoE) takes specialisation a step further by selectively activating different models based on the needs of a specific task. Instead of using a general AI model, like ChatGPT, for all tasks, MoE determines which experts to deploy, enhancing scalability and processing efficiency while maintaining precision.
MoE could be used in healthcare to analyse patient data more effectively, with specialised models focusing on different medical conditions, ensuring that each patient receives a personalised diagnosis.
Agentic AI: Autonomous Decision-Making for Complex Environments
Agentic AI represents the next level of autonomy in AI systems. These systems don’t just provide insights—they make complex decisions and take actions based on predefined goals and real-time knowledge of their environment.
For example, Agentic AI can autonomously adjust vehicle routes in real time, accounting for complex traffic conditions or unexpected delays. The potential of Agentic AI lies in its ability to drive operational efficiencies by making real-time, data-driven decisions at scale.
Graph-Based Models: Mapping Relationships for Smarter Insights
Understanding how things are connected is vital to making smart decisions. Graph-based AI models excel at mapping relationships. For instance, a graph-based AI can analyse social networks to identify influential nodes and predict trends, offering businesses deeper insights and more accurate predictions.
In retail, graph models can optimise recommendation engines by considering the connections between customers, products, and purchasing behaviour and provide more relevant product suggestions.
Choirs: Leveraging Specialised Models for Greater Accuracy
Trideca’s “Choir” architecture integrates multiple models, enabling symphonies of precisely trained Small Language Models (SLM), Specialist Large Language Models (LLM) and other models to both explore a problem and arrive at the very best answer. Like a choir where each voice contributes to form a harmony, in AI, each model explores the problem, combining to deliver a more accurate solution. Within any choir, there can be both harmony and also discord, with the use of graph techniques to enable the exploration of diverging perspectives to identify and evaluate anomalies and gain true insights.
This type of architecture offers extremely high levels of traceability, hallucination and anomaly detection. It presents a dynamic fabric on top of which highly sophisticated solutions can be developed for banking, healthcare, energy, or defence.
Digital Brains: Building AI based on digital synapses & neurons
The Open Brain Institute and Swiss startup Inait have a for-profit arm with leaders in the design and development of an emerging Artificial Intelligence architecture based on adaptive understanding and replicating the way animal brains work. With a recent collaboration with Microsoft, Inait is looking at early commercialisation pilots in risk, finance, and robotic sectors.
Living Intelligence: The Convergence of AI, Biotechnology, and Sensors
The convergence of AI, biotechnology, and sensors provides access to vast new data sources for insights and decision-making.
In healthcare, this could lead to highly personalised treatments, where AI systems continuously monitor a patient’s condition and adjust treatment plans in real-time. In agriculture, it can enable smarter farming practices, with sensors monitoring crop health and AI predicting optimal harvesting times.
What’s Next?
As AI research continues to evolve, new architectures like neural-symbolic networks (which combine the reasoning abilities of symbolic AI with the learning power of neural networks) and self-organising models (which adapt and evolve based on feedback) are on the horizon. Hybrid architectures where humans, Agents, APIs, and more traditional Data science ML are in the cognitive loop will push AI even further, allowing businesses to build systems that learn, adapt, and evolve.
Working with leaders
At Trideca, we’re not just watching these trends, we have established a world-leading AI strategy, ethics, engineering and delivery practice, working across Microsoft, OpenAI, and Open Source LLM architectures employing multi-model, choir and graph-based solutions.
We are already helping businesses integrate these new AI architectures to build smarter products and services that set them apart.