AI Integration & Automation
Put AI to work in your business
Assistants, automations and integrations built around the systems you already run, and on the servers you choose. Your data stays where you put it. Start with one use case, prove it on a pilot, then expand.
Every solution we build can run on your own servers. We tell you exactly what leaves them, if anything.
Where AI really helps
Four kinds of solution we can develop. Each starts from a real task, not a technology.
Knowledge assistants
Your team asks a question in plain language and gets an answer from your own documents, with the sources linked. We decide who can see what before anything sensitive is connected.
Illustrative example
A sales team asks: “What payment terms did we agree with this customer last year?” The assistant finds the signed proposal, quotes the terms and links to the file. It does not show documents the person asking cannot open.
Workflow automation
Data moves between your systems without anyone retyping it. Decisions that need judgement stay with your people.
Illustrative example
A new order arrives by email. Its details go into the ERP, the customer record in the CRM is updated, and the warehouse gets a pick list. Orders above an agreed value wait for a manager's approval.
Business intelligence
Ask how the business is performing and get analysis built on your connected data. Every answer shows where its numbers came from, and what it could not see.
Illustrative example
A director asks: “Why did margin fall in March?” The answer breaks the change down by product line and region, names the reports it used, and notes that March returns are not in the data yet.
AI agents and integrations
AI that carries out a defined task inside your CRM, ERP or other business applications. Before it touches live data, we agree its permissions, limits and approval steps.
Illustrative example
An agent reads incoming supplier invoices, matches each one to its purchase order and drafts the entry in the accounting system. A person reviews every draft before it is posted.
Illustrative example
A project team works in a shared room, and an assistant sits in it. Ask it what was agreed last week and it answers from the room's own messages and files. Ask it to add a task and it does. It can read only what the person asking can read, and nothing in the room leaves the company's server.
Where it runs is your decision
Every solution we build can run in your own environment: on your servers, in your private cloud, or in ours. Your documents, history and the assistant's memory stay where you put them. Only the question sent to the model leaves, and with a model running on your own hardware, nothing leaves at all. We say which before you commit, and we put it in the scope.
Step 1: the assessment
Before anything is built, we look at the task, the data it needs, who can access what, the systems involved and how success will be measured. You get a clear recommendation: go ahead with a pilot, change the approach, or stop.
An AI assessment defines the use case, the data it needs, the integration constraints and the pilot's success criteria. We agree its scope and price before work begins.
Step 2: the pilot
A pilot is small on purpose: a limited scope, a set of real test cases, human review where it matters, and a go-or-stop decision at the end. It shows whether the solution works on your data before you invest more.
Step 3: into daily use
Moving from pilot to daily use means deciding which sources the AI can read, which actions it can take, what a person must approve, and who owns it in operation. We design those boundaries in from the start.
Related work
Tallyca platform
Our own operations platform, with an AI assistant whose outputs stay drafts until a person reviews them.
E-commerce logistics
Order prioritisation, algorithmic inventory segmentation and data-driven pricing. 24% less slow-moving stock.
Questions we often hear
Can AI work with our existing systems?
It depends on what those systems allow. We check the available interfaces, permissions and the task itself before proposing an integration.
Can our data stay in our own environment?
Yes. We design for it from the first day. The software, the database and the files run where you choose, including on your own premises with no internet access to the model if you run one locally. Where a cloud model is the better fit, we define exactly which data reaches it, strip what should not, and agree it in writing before the pilot.
Can the AI make changes on its own?
Only where you decide it can. We define which actions need a person's approval and limit the AI's permissions to match.
How do you know the answers are good?
Before the pilot, we agree representative tasks, success criteria and how mistakes are handled, then measure against them. No AI is right every time, so the design assumes it will sometimes be wrong.
Do you sell a ready-made AI platform?
No. This page describes AI integration services, built around your systems and data.
How does a project start?
With a conversation about the problem. If AI looks like a good fit, the next step is an assessment with an agreed scope and price.
Start with one use case
Tell us the task, the systems involved and what a good result looks like. You do not need a specification.