Introduction and challenges
The IRIS Magi AI enterprise services addresses a critical gap in the market as organisations adopt Generative AI (GenAI), AI, and Machine Learning (ML). As businesses gain confidence in these technologies, the challenge shifts to deploying production-ready solutions that deliver measurable value.
The industry has many challenges facing it including:
- Skills gaps: organisations lack the economic resources to attract and retain highly specialised data science and AI engineering talent
- Rapidly evolving AI technology: managing AI solutions requires continuous adaptation to keep pace with technological advancements
- Performance nuances: Issues like hallucinations, accuracy drift, sentiment and security new approaches to deploying and managing AI applications
- Complex architectures: AI applications increasingly involve composable microservices, large language models (LLMs), retrieval-augmented generation (RAG) databases, and seamless integration with internal and external data sources
Without proper planning, risks such as model drift, security vulnerabilities, and poor user adoption can lead to financial inefficiencies, erroneous advice, or compromised customer experiences. By addressing these challenges with tailored strategies and scalable solutions, IRIS Magi AI enterprise services helps clients safely and effectively deploy and manage AI technologies to drive sustainable growth and innovation.
How we can help
IRIS by Argon & Co has designed Magi, our managed services platform for deploying and managing production-ready AI solutions. Supported by a team of data platform specialists, Magi ensures scalability, security and effectiveness, allowing organisations to focus on optimising operations while Magi handles the complexity of AI management.
Key service elements of our platform:
- Foundation model (FM) monitoring and logging: captures every interaction between users and large language models (LLMs) for full traceability, compliance, and continuous improvement. The Foundation Model Monitoring & Logging Service specialised ITSM function ensures transparency, accountability, and optimisation of AI-driven services.
- Model tuning: uses automated techniques to maintain model accuracy, efficiency, and security while preventing performance drift. This service ensures outputs remain relevant and aligned with business needs, critical for the integrity of AI operations.
- Artificial intelligence /machine learning operations (AI/ ML Ops): provides automated, integrated deployment and management of AI applications through continuous integration/continuous delivery (CI/CD) pipelines. This service supports the entire AI lifecycle, ensuring reliability, scalability and seamless updates.
- Financial operations (FinOps): tracks cloud infrastructure costs, token consumption, and anomalies to optimise financial performance. This ensures cost-effectiveness and budget compliance while maintaining service excellence.
- Reporting: delivers a comprehensive suite of reports, tracking KPIs like accuracy, sentiment and service level objectives (SLOs). Monthly performance updates and bi-annual improvement insights ensure continuous optimisation.
Our service can be deployed via the cloud or on premise and architected to operate on all major cloud platforms including Microsoft and AWS. Clients are able to focus on optimising their business, leaving the complexity of managing AI solutions to IRIS by Argon & Co and the Magi platform