AI & ML

The challenge

AI has moved past the proof-of-concept stage. The organizations gaining real value are the ones running AI workloads in production — not demoing chatbots in a sandbox. The gap between a promising prototype and a reliable, secure, cost-efficient production service is where most AI initiatives stall.

Bridging that gap requires more than data science. It requires cloud architecture, MLOps discipline, responsible AI guardrails, and a clear understanding of which Microsoft AI platform components to use and how they fit together.

Production AI on Azure

Mallow builds AI solutions that run in production. We have delivered AI platforms, intelligent agents, and RAG-based applications for organizations with strict security and compliance requirements — including Telia's AI platform, where we built a secure, code-based environment for AI service development and testing.

Azure AI Foundry

Azure AI Foundry is Microsoft's unified platform for building, evaluating, and deploying AI applications. We use Foundry to orchestrate model selection, prompt engineering, evaluation pipelines, and deployment — giving teams a structured path from experimentation to production.

• Model catalog and fine-tuning workflows
• Prompt flow for orchestrating multi-step AI pipelines
• Built-in evaluation and safety testing before deployment
• Integration with Azure OpenAI Service models

Semantic Kernel and AI agents

Microsoft Semantic Kernel is the framework for building AI agents that combine LLM reasoning with enterprise data and actions. We use Semantic Kernel and the Microsoft Agent Framework to build agents that go beyond simple chat — agents that retrieve data, call APIs, execute workflows, and make decisions within defined guardrails.

• Autonomous and semi-autonomous AI agents for business processes
• Plugin architecture connecting agents to enterprise systems and APIs
• Multi-agent orchestration for complex workflows
• Memory and context management for stateful conversations

RAG architecture

Retrieval-Augmented Generation grounds AI responses in your own data. We design and implement RAG pipelines that combine Azure AI Search with Azure OpenAI to deliver accurate, source-cited answers from enterprise documents, knowledge bases, and structured data.

• Chunking strategies, embedding models, and vector index design
• Hybrid search combining semantic and keyword retrieval
• Source attribution and citation in generated responses
• Data ingestion pipelines for documents, databases, and APIs

What we deliver

• AI strategy and use-case identification tied to measurable business outcomes
• Production AI applications using Azure AI Foundry, Semantic Kernel, and Azure OpenAI
• Intelligent agents and multi-agent systems built on the Microsoft Agent Framework
• RAG solutions grounding AI in enterprise data with Azure AI Search
• MLOps pipelines that automate training, evaluation, deployment, and monitoring
• Responsible AI assessments covering fairness, transparency, and data privacy

How we work

We do not start with the model — we start with the problem. Every engagement begins by identifying where AI creates the most business value, then building iteratively toward production. We deliver working software early and refine it in subsequent sprints.

Production readiness is not an afterthought. We build MLOps pipelines from day one, design for cost efficiency at scale, and ensure your team can operate and evolve the solution independently.

From our insights

• Building Production AI Agents with Semantic Kernel, .NET Aspire, and Azure OpenAI — Tomi Asp walks through building enterprise AI agents with a pro-code approach, covering plugins, tool calling, and token management.
• How to Implement AI Right Now — Practical steps for organizations looking to adopt AI with Azure.

Key technologies

• Azure AI Foundry
• Azure AI Agents Service
• Azure OpenAI Service
• Microsoft Semantic Kernel
• Microsoft Agent Framework
• Azure AI Search
• Azure Machine Learning
• Azure AI Services