Scaling Multi-Agent AI Ecosystem for a Global Industrial Leader

A Global Industrial Technology Leader

Our client is a world-recognized leader in heat transfer, centrifugal separation, and fluid handling technologies, with a heritage spanning well over a century. With operations across more than 100 countries and a global workforce of more than 15,000 employees, the company supplies highly engineered, custom-configured solutions to industries ranging from energy and chemicals to food processing, marine, and HVAC.

The company’s product portfolio includes complex, application-specific equipment such as customized heat exchangers, which require deep technical expertise to configure correctly. This complexity, combined with the organization’s global scale, creates significant operational demands across service, sales, legal, and support functions.

Headquartered in Sweden, the company had already embarked on an ambitious internal AI roadmap and was actively seeking a technology partner capable of delivering both hands-on AI engineering and strategic AI advisory at enterprise pace.

Scaling AI Across a Complex Global Enterprise

The client faced a dual challenge that is increasingly common among large industrial enterprises: they had identified high-value AI use cases but lacked the internal capacity and architectural foundation to execute on them at speed.

Specifically, the organization was dealing with several compounding pain points:

• Fragmented AI capabilities with no unified platform. Initial AI initiatives were siloed across functions. Two discrete applications, focused on document analysis and heat exchanger configuration support, existed independently with no shared infrastructure or governance framework.
• Over-reliance on predefined, rigid workflows. An early chatbot deployment, originally initiated by a major software vendor, relied on hard-coded, predefined response workflows. This approach limited flexibility, slowed iteration, and made scaling to new use cases operationally expensive.
• Vendor lock-in and rising costs. The client’s growing AI investment was concentrated within a single vendor ecosystem. As AI use expanded, so did platform costs, prompting leadership to evaluate more cost-effective, architecture-agnostic alternatives.
• Lack of a scalable agent development framework. As individual business units recognized the value of AI, demand for new agents grew rapidly. Without a common framework for building, deploying, and governing agents, each new use case required a disproportionate investment of time and resources.
• No centralized interface for end users. Employees across service, sales, legal, marketing, and finance were using disconnected tools. There was no unified entry point that could route queries intelligently across multiple AI agents.

Underlying all of these challenges was a strategic imperative: the client needed to move from point-solution AI experiments to a coherent, enterprise-grade AI platform capable of supporting continuous innovation across every business function.

A Two-Track Engagement: Sustaining Legacy AI While Building the Future Platform

Algomine’s engagement with the client ran across two parallel, complementary workstreams over a period of more than two years, covering the full spectrum from legacy AI maintenance to greenfield multi-agent platform development.

Stream 1: AI Application Maintenance and ML-Powered Configuration Support

In the first workstream, Algomine provided dedicated support for two production AI applications: a document analysis tool and a configuration assistant for the client’s bespoke heat exchanger product line. These applications had previously relied on individual subject-matter experts who possessed the institutional knowledge required to guide configurations correctly.

Our team worked directly alongside the client’s engineers to encode this specialized domain knowledge into machine learning models, reducing dependency on individual expertise and enabling consistent, scalable configuration support. This workstream established the foundation of trust that would define the broader partnership.

Stream 2: AI Service Agent, AI Center of Excellence, and Enterprise Platform

The second workstream was transformative in scope. Algomine took over a service chatbot originally initiated by a third-party software vendor and rebuilt it from the ground up as a production-grade AI assistant, the AI Service Agent, designed to support both field service technicians and sales teams.

Key capabilities of the AI Service Agent included intelligent retrieval of spare parts information via live API integrations, contextual support from synchronized product documentation including PDFs and brochures, and improved spare parts matching to reduce both resolution time and customer friction.

Following its production deployment at the end of 2025, the AI Service Agent immediately achieved thousands of unique daily active users, validating the business case for further expansion.

As demand from other business units grew, Algomine helped the client establish a formal AI Center of Excellence (AI CoE) to govern agent development, maintain architectural consistency, and accelerate the delivery of new AI capabilities. Under this framework, the team delivered a growing roster of specialized agents:

• AI Service Agent: intelligent support for field service and spare parts management
• Ask Legal: on-demand legal guidance for internal teams
• Generic Enterprise Agent: general-purpose assistant for company-wide knowledge retrieval
• Branding Agent: AI-powered copywriting and brand consistency support for marketing teams

The Architectural Shift: From Predefined Workflows to Agentic Reasoning

One of the most significant contributions Algomine made was driving a fundamental architectural evolution in how agents are built and operated. The original vendor-delivered chatbot relied entirely on predefined, scripted response workflows. Every answer was hard-coded, every decision tree manually maintained.

Algomine replaced this model with a tool-based agentic architecture. Rather than scripting responses, agents are now equipped with a set of callable tools including live APIs, SharePoint repositories, and external data sources, and reason dynamically over them to construct contextually appropriate answers. This shift dramatically accelerated development velocity: whereas early releases involved incremental fixes to individual agents, each subsequent release now introduces a fully new, independently operating agent into the platform.

The Unified Platform: A ChatGPT-Style Interface for Enterprise AI

As the agent portfolio grew, the need for centralization became clear. Algomine contributed the AI backbone of a unified enterprise platform, a single interface through which employees can interact with any available agent, built collaboratively with another technology partner responsible for the front-end and back-end infrastructure.

This platform, deployed to production at the end of 2025, functions as the client’s internal AI hub: one entry point, multiple specialized agents, seamless user experience.

Technology Highlights

Enterprise-Wide AI Impact, Measurable from Day One

• Immediate and sustained user adoption. The AI Service Agent achieved thousands of unique daily users upon production launch, reflecting a tool that addressed genuine operational needs rather than a pilot with limited reach.
• Elimination of vendor dependency and cost reduction. By transitioning away from the original third-party platform and building on an architecture-agnostic AI backbone, the client reduced vendor lock-in and gained long-term cost flexibility, a strategic priority driven by the client’s Sweden-based leadership.
• Dramatic acceleration of agent delivery. The shift to tool-based agentic architecture reduced the time and effort required to deliver new agents. The cadence moved from one-off fixes to a continuous delivery model, with each new release adding a net-new specialized agent to the platform.
• Scalable governance via the AI Center of Excellence. The AI CoE framework provides the client with a repeatable model for AI innovation, enabling business units across the organization to request, define, and receive purpose-built AI agents without starting from scratch each time.
• Cross-functional AI coverage. From field service and spare parts to legal, marketing, and internal knowledge management, AI capabilities now span the full enterprise, creating consistent productivity gains across diverse teams.
• A future-ready foundation for Multi-Agent Orchestration. The platform is architecturally positioned for the next stage of maturity: agents that communicate and collaborate with one another via MCP-based integrations. Finance and accounting agents, for example, are already being designed to synchronize data and reasoning across functions, a capability that positions the client at the leading edge of enterprise AI.

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Whether you are running your first AI pilot or scaling toward multi-agent orchestration, Algomine has the architecture, the engineering depth, and the track record to get you there – contact us.