AI Applications
AI integration, orchestration, and consultation
Key Capabilities
- AI implementations in web applications for specific business tools
- Orchestration layers connecting to multiple systems for business automations
- AI adoption consultation and opportunity identification for business process automation
- Large Language Model fundamentals education and statistical behaviour understanding
- Retrieval Augmented Generation (RAG) architecture development for implementation in applications
- Model Context Protocol (MCP) development and integration and tool usage
- Prompt engineering techniques for effective agent behaviour
- Non-deterministic system testing and validation methodologies
AI Applications
Artificial intelligence, particularly large language models and agent-based automation, represents a technological inflection point comparable to the internet's emergence in business during the late 1990s. There is an entire younger generation familiar with these tools and technologies, while those over 40 are still discovering ChatGPT.
Within five years, AI integration will transition from competitive advantage to operational necessity, much as web presence evolved from novel to essential. Organisations exploring AI capabilities today position themselves before industry standards solidify and the technology becomes commodity infrastructure. The question is not whether to investigate AI adoption, but whether to begin exploration now while the landscape remains fluid, or later when established practices limit options.
Dagny Labs approaches AI from an early adopter perspective, with services focusing on hosted and self-hosted orchestration layers for business processes and custom AI applications running within web applications.
Consultation and education is also provided — helping organisations understand that Large Language Models represent a statistical language tool, what becomes possible with prompt engineering, and how to test and secure these systems. How they can offer business optimisations that may not have been considered. What becomes possible when extended with Retrieval Augmented Generation for handling large contexts, and Model Context Protocol implementations for AI interfaces to APIs. How to make this more cost effective by using different foundation models, and providing data sovereignty through self-hosting open source models.
The differentiator lies in applying twenty years of industrial engineering practice to AI system development — using this technology standalone, integrated within business operations, and to enhance engineering productivity in the other domains on offer.
Industry Experience
AI system development since 2023, coinciding with large language model emergence as a practical tool, focuses on prototyping and consultation. Mathematical understanding of how LLMs function as statistical tools in a multi-dimensional vector space navigated through prompt engineering informs realistic assessment of appropriate use cases versus limitations.
Reach Out
Want to learn more about AI or want a custom application? Please make contact to see if there is a fit.