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Struck AI Usage
May 1, 2025
Created by
Struck Team
Communication
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Struck AI Usage
Struck utilises a modular approach to AI, selecting the best model for a given application based on its unique strengths. This enables more tailored, efficient, and powerful responses compared to generalist systems like ChatGPT or Microsoft Copilot.
Why Multiple Models?
Different AI models offer different capabilities:
Multimodal capabilities (e.g. processing images and tables) are only supported by some.
Context window limitations can affect how much text a model can handle.
Performance trade-offs exist — for example, O1 excels in complex reasoning but is slower and more limited in availability.
Gemini offers both “Flash” and “Pro” editions, where Flash is faster but less nuanced, and Pro is better at handling complex or large-context tasks.
Analogy: Think of Struck’s system as a team of specialists, each chosen based on the task, whereas generalist AI services operate like an army of one-size-fits-all responders.
Agentic AI at Struck
Struck uses an agentic AI approach, where tasks are defined as goals, and the system is given decision-making ability to pursue them.
Example: At Loman’s, a tender evaluation system uses a set of standardised questions to generate a detailed report on the attractiveness of a project.
Privacy Considerations
Struck prioritises data privacy by using models hosted in Europe, avoiding exposure to U.S. data regulations. All model providers have agreements that prohibit training on user data.
Struck AI Usage
Struck utilises a modular approach to AI, selecting the best model for a given application based on its unique strengths. This enables more tailored, efficient, and powerful responses compared to generalist systems like ChatGPT or Microsoft Copilot.
Why Multiple Models?
Different AI models offer different capabilities:
Multimodal capabilities (e.g. processing images and tables) are only supported by some.
Context window limitations can affect how much text a model can handle.
Performance trade-offs exist — for example, O1 excels in complex reasoning but is slower and more limited in availability.
Gemini offers both “Flash” and “Pro” editions, where Flash is faster but less nuanced, and Pro is better at handling complex or large-context tasks.
Analogy: Think of Struck’s system as a team of specialists, each chosen based on the task, whereas generalist AI services operate like an army of one-size-fits-all responders.
Agentic AI at Struck
Struck uses an agentic AI approach, where tasks are defined as goals, and the system is given decision-making ability to pursue them.
Example: At Loman’s, a tender evaluation system uses a set of standardised questions to generate a detailed report on the attractiveness of a project.
Privacy Considerations
Struck prioritises data privacy by using models hosted in Europe, avoiding exposure to U.S. data regulations. All model providers have agreements that prohibit training on user data.
Struck AI Usage
Struck utilises a modular approach to AI, selecting the best model for a given application based on its unique strengths. This enables more tailored, efficient, and powerful responses compared to generalist systems like ChatGPT or Microsoft Copilot.
Why Multiple Models?
Different AI models offer different capabilities:
Multimodal capabilities (e.g. processing images and tables) are only supported by some.
Context window limitations can affect how much text a model can handle.
Performance trade-offs exist — for example, O1 excels in complex reasoning but is slower and more limited in availability.
Gemini offers both “Flash” and “Pro” editions, where Flash is faster but less nuanced, and Pro is better at handling complex or large-context tasks.
Analogy: Think of Struck’s system as a team of specialists, each chosen based on the task, whereas generalist AI services operate like an army of one-size-fits-all responders.
Agentic AI at Struck
Struck uses an agentic AI approach, where tasks are defined as goals, and the system is given decision-making ability to pursue them.
Example: At Loman’s, a tender evaluation system uses a set of standardised questions to generate a detailed report on the attractiveness of a project.
Privacy Considerations
Struck prioritises data privacy by using models hosted in Europe, avoiding exposure to U.S. data regulations. All model providers have agreements that prohibit training on user data.
