Enterprise AI
Many organisations are experimenting with AI. Harmony makes sure AI can actually act. By connecting systems, data, processes and governance, we make AI an operational part of your organisation.
Harmony's clients include
Who it is for
The architecture
Smart AI agents don't work on their own. They connect systems, understand context, take actions and involve people when needed. With our experience in integration, application development and enterprise architecture, we make sure that whole stack works together securely and at scale.
Work directly with business data.
Result: Reliable answers and actions based on current data.
More than just applications.
Result: Better-grounded answers and decisions.
Governance built in.
Result: Secure automation at scale.
People and AI work together.
Result: Higher efficiency, while keeping control.
Our approach
No ten-week strategy deck. In the Harmony AI Agentic Lab we choose one use case together, connect it to your systems and show what there is to gain.
Together we find the process where AI measurably frees up time, and check whether it is worth tackling.
We make an ROI estimate and map out the costs of a first MVP or proof of concept. The prices of AI tooling still move a lot, so we do that up front.
We design how the agent connects to your systems and set down roles, access rights, guardrails and audit trails, following the principle of governance by design.
We build a prototype that works with your own data and systems, so the result is visible before anything more is invested.
We decide how the solution grows in a controlled way: from an agent that advises to more autonomy, only where that is responsible.
Sectors
In every sector, the value of AI lies in the connection with the systems where the work happens. Below you can see what AI agents do in each sector.
Another sector? No problem. AI agents help wherever staff have to bring together information from several systems.
Talk to an expertFrom district nursing to lab requests: AI agents combine data from EHRs, care plans and operational systems, so the administrative burden goes down and action follows faster.
Combines information from ONS, calendars and care plans, flags deviations and prepares reports with the right context. Building blocks: FHIR, ONS APIs, event-driven architecture and real-time data processing.
Understands the context of the diagnosis, prepares the request and processes it directly in the EHR. Building blocks: FHIR Condition, HL7, Omni Gateway and MCP.
Access to information from multiple source systems, without duplicate storage or synchronisation, and always up to date. Building blocks: MuleSoft, MCP servers, A2A connections and API integrations.
Agents support staff with assessment, building case files and claims handling, by bringing information together and applying policy rules.
Flags unusual claim patterns, checks against guidelines and policy conditions and gives a substantiated risk assessment. Building blocks: machine learning, LLM expert layers and rule engines.
Builds a complete case file overview from customer, policy and claim information and answers questions in plain language. Building blocks: RAG, document intelligence and API integrations with core systems.
Analyses submitted documents, checks for missing information and prepares underwriting proposals. Building blocks: OCR, document AI, workflow orchestration and policy engines.
From customer contact to order processing: AI agents pull information from ERP, CRM and operational systems, so processes run faster and errors further down the line are avoided.
Combines customer, contract and invoice data, answers operational questions directly and supports staff during customer contact. Building blocks: Salesforce, SAP, Dynamics, RAG and real-time API connections.
Answers questions about orders and processes with current data from the source systems, without manual searching. Building blocks: MCP, enterprise search, vector databases and RAG.
Checks orders before they go to the ERP and flags missing or inconsistent data. Building blocks: workflow engines, business rules, CPQ integrations and ERP connectors.
Frequently asked questions
Agentic AI goes further than a chatbot. AI agents retrieve information on their own, consult systems, support decisions and carry out actions, within set processes and permissions. That gives you a digital colleague that does not just advise, but also gets work done in the applications you already use.
AI initiatives often focus on the model, while the biggest challenge lies in the connection with processes, data and systems. Without a connection to existing workflows, AI stays limited to isolated experiments. Agentic AI that works therefore starts with a strong integration architecture and a clear governance framework.
We build AI solutions on top of the systems you use today. With our experience in MuleSoft, Frends, API management and application development, we connect agents to ERP, CRM, HR or other business systems. That way you build on existing investments and a solution reaches production faster. Depending on the use case, agents work directly with current data from the source systems, or through optimised AI indexes.
Yes. AI agents support or automate both repetitive and knowledge-intensive tasks, such as processing requests, gathering information, preparing case files, supporting customer processes or guiding staff. They always work within set business rules, permissions and agreements.
As autonomous as is responsible, and we build that up step by step. An assisting agent advises and a person decides. A supervised agent carries out actions after approval. An agent with bounded execution does low-risk tasks itself, within fixed limits. Only after that do several agents coordinate a process together, with monitoring and escalation to a person.
Security is the foundation of every AI solution we build. We connect to existing roles, access rights, audit logs and security standards, so AI agents work under the same governance as other business applications. For regulated sectors such as healthcare and government, we take into account the GDPR, NIS2, the European AI Act and existing information security guidelines, among others.
Getting an AI agent to run in a demo is no longer hard. Getting an agent to work securely in business-critical systems is a different step: arranging permissions and governance, connecting to current data in your EHR, CRM or ERP, and making sure the agent holds up in your architecture. That is what Harmony does, and that difference decides whether AI stays a demo or becomes a digital colleague.
No ten-week strategy deck. We choose one use case together, connect it to your systems and show what there is to gain.
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