Email & document processing
Automatically classify, summarise and extract data from incoming emails, invoices, contracts or CVs — straight into your systems.
Built in practice: MailHarbor
AI agents
AI agents that actually get work done
Software that performs tasks autonomously: processing emails, analysing documents, answering questions from your own data, and operating your systems — always under your control.
What is an AI agent?
More than a chatbot: a digital co-worker that performs tasks inside your systems.
An AI agent is software that combines a Large Language Model (LLM) — such as GPT, Gemini or Claude — with your business systems. Where a chatbot only talks, an agent acts: it reads your mailbox, queries your databases, calls APIs and processes documents.
The agent understands an instruction in plain language, breaks it down into steps and executes them autonomously. Think of an incoming invoice that is automatically recognised, verified against the purchase order and staged in your accounting package — without anyone having to look at it.
Importantly, a well-built agent operates within strict boundaries. You decide what it is allowed to do, which data it can see, and when a human needs to approve. You get the speed of AI with the control of traditional software.
Chatbot
- Answers questions
- Only knows general knowledge
- Waits for input
- No access to your systems
AI agent
- Performs tasks
- Works with your own data (RAG)
- Acts proactively and on schedule
- Securely connected via APIs
What does an AI agent actually do?
A few applications already delivering value for SMEs and service businesses today.
Knowledge assistant on your data (RAG)
Employees ask questions in plain language and get reliable answers from your own documents, manuals and databases — with source references.
Explore the approach and limitations of RAGCustomer service support
A first line that answers questions 24/7 based on your knowledge base and hands complex cases over to a human colleague.
Workflow automation
An agent executes multi-step processes across systems: from quote follow-up to order processing in your ERP or CRM.
Reporting & analysis
Recurring reports and analyses that write themselves: the agent gathers figures, detects anomalies and summarises them clearly.
Secure system integration
Integration with the latest APIs from OpenAI, Gemini and Claude — within your security rules, with logging and access control.
How do we approach it?
Start small, validate fast, and scale under control.
1
Analysis
We map your processes and pick the task where an AI agent delivers value fastest.
2
Proof of concept
Within weeks, a working agent runs on a well-defined task, tested on your real data.
3
Integration & security
The agent is securely connected to your systems, with access control, logging, and human approval where needed.
4
Monitoring & improvement
We track quality, refine prompts and tools, and expand step by step to new tasks.
Why invest in an AI agent?
- Win back hours of repetitive work per week for your team
- Fewer errors thanks to consistent, controlled processing
- Available 24/7, including outside office hours
- Scalable: one agent processes ten or ten thousand documents
- Fast ROI by starting small with a proof of concept
- Control over data: agreed access, retention and model-training settings
Frequently asked questions
Is an AI agent the same as a chatbot?
No. A chatbot only answers questions. An AI agent performs tasks autonomously: it reads emails, queries databases, calls APIs and processes documents. A chatbot talks; an agent works.
Is my business data safe?
That depends on the architecture and providers. We agree on hosting, retention and data use up front, restrict access and include logging. Whether data is used for model training is addressed in the configuration and agreements with the selected provider.
Which systems can an agent connect to?
Almost anything with an API or database: Outlook/Exchange, SharePoint, ERP and CRM systems, accounting packages, ticketing tools and custom software. Legacy systems can be unlocked through an intermediate layer.
What does an AI agent cost?
It depends on the scope. We usually start with a compact proof of concept so you can validate quickly and affordably whether the agent delivers value, then expand deliberately.
How quickly is an agent operational?
A first working proof of concept is typically up within a few weeks. A production-ready agent with security, monitoring and integrations follows in short iterations.
Ready to automate with AI?
Discuss, without obligation, which task in your business is the best fit for a first AI agent.