AI Agents for Business
The work that repeats every single day, done before you get to it — and handed over ready to approve.
An agent reads the mail as it arrives, separates what is urgent, works out what it is about and drafts the reply with that client's history to hand. It does the same with documents, requests and deadlines.
It connects to the systems you already have. It does not replace your tools — it works inside them.
- Over 25 years of IT experience
- The agent prepares, the approval is always yours
- The agent respects the permissions you already have in place
- Connects to the systems you already use
- A support team under contract, not just an implementation
- Cost per use case estimated before we start
A working day, automated
The simplest way to explain an agent is to follow one email from the moment it lands until the end of the day.
08:00 — Reads what came in
Goes through the mail that arrived overnight and separates three things: what needs an answer today, what can wait, and what is information that asks for nothing.
08:02 — Works out what it is about
Quote request, fault report, invoice, complaint, job application, spam. Each type goes to whoever handles it, without passing through everybody's inbox.
08:05 — Drafts the reply
For the cases that repeat, it leaves a draft already carrying that client's history and the figures it needs to quote. It leaves it ready — it does not send it.
08:10 — Flags what matters
An important client has written. A request has gone three days without an answer. An order has slipped past its usual lead time. What is exceptional gets flagged; what is routine does not.
18:00 — Closes the day
A summary of what came in, what is still unanswered and what needs a decision tomorrow. One document, not a full inbox.
The same path applies to documents arriving, to requests coming in through the website, or to deadlines approaching. Email is simply the most recognisable example.
The agent prepares. The decision is yours.
The first question everyone asks about this is always the same: what if it sends something stupid to one of my clients?
The answer is that it does not, because it is not the one sending. The agent stops before any action with consequences outside the company and waits for confirmation.
- Automatic — reading, classifying, organising attachments, creating tasks, flagging deadlines
- Requires approval — replies to clients, proposals, figures, sending documentation
- Restricted — financial commitments, contracts, price changes, final technical opinions and legal communications never go out on their own
- The three levels are agreed with you in writing, before we start
- There is a record of who approved what, and when
This is not a rule someone wrote into a workflow that a later change can quietly undo — it is how the platform works. It is also why we do not promise zero errors: we promise that no error leaves the building without a person seeing it.
Where an agent pays off
These are the cases where we see real returns. Each one starts with a diagnosis of your specific situation — no two companies have the same workflow.
Email triage and drafting
Reads the inbox as it fills, separates urgent from not urgent, categorises by type and prepares drafts carrying the client's history. Flags whatever has gone unanswered.
For anyone who receives a lot and answers late.
Document extraction
Invoices, orders, delivery notes and PDFs read and turned into data ready to enter your management systems. At volume, batch processing costs roughly half.
For anyone receiving hundreds of documents a month.
An assistant over your own documentation
Answers from your manuals, contracts and procedures, and cites the source of every answer. It is not «the AI says so» — it is «it is in clause 4 of the contract».
For teams losing time hunting for information that already exists.
First-line support
Answers what gets asked repeatedly — on the website, by email or on WhatsApp — and escalates to a person when it falls outside the expected. The escalation rules are yours.
For anyone answering the same ten questions every day.
Automatic reports and summaries
The agent produces the file — Excel, Word or a presentation — covering what came in, what is pending and what needs a decision.
For anyone spending hours compiling what is already in the systems.
Monitoring and alerts
Requests unanswered for too long, deadlines approaching, patterns outside the usual. It runs on its own and tells whoever needs to know.
For whatever tends to fail through oversight rather than lack of capacity.
Proposals and commercial documents
Drafts built from your own templates and the client's history, ready to review and sign.
For sales teams losing deals to slow proposals.
It connects to what you already use
An agent is only useful if it can reach the company's real information. You do not need to change tools — we work with the ones you already have.
Communication
- Microsoft 365 — mail and calendar
- Microsoft Teams
- WhatsApp and support channels
Documents
- SharePoint and OneDrive
- Network folders and archives
- PDFs, spreadsheets and scans
Business
- Management and invoicing software
- CRM and client history
- In-house systems, through your API
Where no ready-made connector exists, we build the integration. It is the same work we have always done — only now there is an agent on the other side.
Not everything should be an agent
A task with fixed rules — if the subject contains this, file it there — is cheaper, faster and more reliable as plain automation. It needs no artificial intelligence at all.
An agent pays off when the decision requires interpreting what is written:
- Recognising that an email is urgent without it using the word «urgent»
- Telling a complaint apart from a request for information
- Reading an invoice in a format it has never seen before
- Summarising a long thread and pulling out the decision that was actually taken
- Answering a question whose answer is spread across three documents
We will tell you when it is not worth it. We would rather lose a project than install something that costs you more than it saves.
Plain automation, or an agent?
The two solve different problems, and they often live side by side in the same company.
| Plain automation | AI agent | |
|---|---|---|
| How it decides | Rules someone wrote | Interprets the content |
| When it fails | When an unforeseen case turns up | When the request is ambiguous — and then it asks |
| Cost | Fixed and low | Per use, estimated beforehand |
| Best for | Moving, copying, notifying | Reading, classifying, drafting, summarising |
If your case can be solved with rules, we will say so and build you the plain automation.
We do not sell an agent to someone who needs a mail filter.
Your data
A company mailbox contains personal data. An agent reading it is processing personal data, and that carries obligations that are not optional.
Which is why we settle this before writing the first line of configuration:
- We agree with you, in writing, which data may be sent for processing and which stays out
- In a Microsoft-native implementation, processing never leaves your own Microsoft environment
- Access uses least-privilege application credentials, limited to what the use case requires
- The agent respects the permissions already in place: nobody starts seeing what they could not see before
- A record of the actions carried out and of who approved them
- Retention periods, excluded document types and the legal basis assessed before we start
We can support the technical assessment of the processing, but that does not replace legal advice or the work of a data protection officer.
How we implement
01
Diagnosis
We work out where the time is going. We come away with a list of candidate use cases and a monthly cost estimate for each.
02
A pilot on one case
We pick one process and put it to work in a real environment, with human approval on everything that goes outside. It gets measured before it gets widened.
03
Connecting the systems
We configure the access, define what requires confirmation, and leave a written record of the agreed scope.
04
Operation and maintenance
Models change and integrations break. We monitor, fix and adjust — with the same team that already handles your support.
What it costs
An agent has two costs: the implementation, which happens once, and the usage, which is monthly and proportional to volume.
The second is the one almost nobody mentions up front. We estimate it during the diagnosis, based on your company's real volume:
- How many emails, documents or requests per month
- How much requires full reading and how much is quick triage
- Whether there are seasonal peaks
- Which tools you already pay for that can be put to use
Some of the work is cheaper than it looks: context that repeats across every call is reused and costs a fraction, and batch processing costs roughly half. That goes into the estimate.
Real cases
Two different sectors, the same principle: the agent organises and prepares, the person decides. Each case describes the full process, the autonomy levels agreed and what the agent never does on its own.
Frequently asked questions
No. The agent prepares and stops. Sending, deleting or changing anything requires a person to confirm. The actions that need approval are agreed with you before we start.
No. We connect to what you already use — mail, documents, management, CRM. Where no ready-made connector exists, we build the integration.
It will, like any system. That is exactly why nothing with consequences outside the company goes out without human approval, and why we start with a pilot on a single case, measured in a real environment before widening it.
It depends on the solution. In a Microsoft-native implementation, processing happens inside your company's own Microsoft environment and the data never leaves it. Where the AI engine is external, we agree in writing — before we start — which data may be sent for processing, which documents stay out, and how long anything is retained. That assessment is part of the diagnosis.
No. A chatbot converses. An agent executes: it reads, classifies, queries systems, produces files and prepares actions. Automated customer service is just one of the use cases.
It depends on the case and on the connections required. A single-process pilot is deliberately short — the goal is to measure early, not to deliver everything at once. The schedule comes after the diagnosis.
That is the normal path. You start with one case, measure it, and only then widen. Each new process gets assessed the same way, including whether it should be an agent at all.
The same team that already supports hundreds of companies. We do not hand over a project and disappear — models change, integrations break, and that needs looking after.
Start with one process, not with a revolution
The quickest way to find out whether this makes sense for you is to pick one task that repeats every day and measure what it costs today.
We run that diagnosis with you and tell you frankly whether it is worth it — including when the answer is that it is not.
Need any clarification?
Tell us your challenge. We reply quickly and with no commitment whatsoever.
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