AI & Business Automation
AI and automation that take repetitive work off your team.
Your team may be typing the same details into two systems, handling invoices and documents by hand, or answering the same internal questions every week.
We find the parts of that work that follow clear rules, automate them and connect the systems involved. Where AI is used, a person reviews anything that matters before it takes effect.
An example process. A new enquiry arrives from the website. An AI assistant drafts a summary and a reply. The enquiry is added to the CRM, the team is notified and a follow-up is drafted. A person checks the follow-up before it is sent.
Is your process a good place to start?
Some processes are worth automating and some are not. These signs help you judge before anything is built.
Usually a good candidate
- It is repeated often, every day or every week.
- It follows rules that can be written down, even with exceptions.
- The information it needs already exists in documents or systems.
- A mistake can be caught before it causes harm.
- Someone owns the process and can say what "done right" means.
A poor first candidate
- Every case is a judgement call.
- The data is scattered, incomplete or missing.
- A single error would be costly and hard to detect.
If your process looks more like the second list, we'll tell you before anything is built.
What we build and connect
Assess
Process discovery and an AI-readiness assessment, a map of how the work runs today, and a list of what to automate first, ranked by value and risk.
Connect
Integrations between your customer relationship management (CRM) system, your enterprise resource planning (ERP) system and other tools, and document and invoice processing that feeds them.
Assist
Internal knowledge assistants, AI-assisted case and support workflows, applications built on large language models (LLMs) and retrieval-augmented generation (RAG), human-supervised agentic workflows and other custom AI integrations.
Operate
Monitoring after launch, security, regular checks that the system still gives good answers, and support.
When an automation needs its own application or a deeper integration, it becomes a Custom Software Engineering project.
What we mean by a custom AI assistant
When we say custom AI assistant, we mean software that works with your organisation's own information, respects who is allowed to see what, and fits into the way your team already works. It answers from your approved documents or systems, not from general knowledge alone, and it shows where an answer came from where that is possible. It is not a chatbot with your logo on it.
In plain terms, a large language model writes the answer, and retrieval-augmented generation means it first looks up the relevant passages in your own material and answers from those.
Three examples of what that can look like:
- an employee knowledge assistant that answers from approved documentation
- a sales assistant that pulls the relevant CRM context before a call
- a support assistant that drafts replies for a person to approve
How people stay in control
- 01
Suggest
The system proposes; a person decides and acts. For example, a drafted support reply or a suggested category for a document.
- 02
Approve before action
The system prepares an action, and a person approves it before anything changes. For example, posting an invoice or updating a customer record.
- 03
Automatic with oversight
For low-risk steps that can be undone, the system acts and logs what it did, and people review samples and exceptions.
Financial, HR and other decisions with serious consequences never go beyond the second level: a person decides.
The assistant sees only what the person using it is allowed to see. When it is unsure, or a source is missing, it says so and hands the case to a person. Every automation is tested on real examples before launch and checked again after it. Which AI model providers are used, and where data is processed, is agreed for each project, with your data protection requirements as a constraint.
Automation reduces repetitive work and some kinds of error. It does not remove every error, and it does not pay off for every process; the assessment is there to show which ones it suits.
Typical situations
Example situations, not client projects
An employee knowledge assistant
Imagine a company whose staff ask the same questions about procedures and product details every day. An assistant answers from the approved documentation, shows which document each answer came from, and says so when the documents don't cover the question. Updating the documentation stays with the people who own it.
A sales assistant with CRM context
Before a call, a salesperson asks for a summary of the customer: recent orders, open issues and the last conversation. The assistant pulls this from the CRM, limited to what that salesperson is allowed to see. The salesperson decides what to do with it.
Support replies drafted for approval
A support team answers many similar emails. The assistant drafts each reply from the order details and the support guidelines. Someone on the team reads it, changes it if needed and decides whether to send it.
How an automation project runs
- 01
Discovery call
We talk through the process, who does it and what goes wrong. You'll know whether it looks like a reasonable place to start.
- 02
Process assessment
We map how the work runs today, step by step, including the exceptions people handle by hand.
- 03
Business case and risk review
We set out what automating it would involve, how much manual effort it could remove and what could go wrong. If the case is weak, this is a good place to stop.
- 04
Limited pilot, where appropriate
A small version runs on a sample of your own cases, with people checking every result, before anything is relied on.
- 05
Scoped implementation
The full automation is built to the agreed scope, with the levels of control agreed in the risk review.
- 06
Optional support
After launch, we can monitor the automation, check its results and adjust it as your process changes.
Questions people ask
Where does our data go?
It is agreed for each project: which AI model providers are used, where the data is processed, and how your data protection requirements are met. The assistant only sees what its user may see.
What happens when the AI gets something wrong?
It will sometimes, which is why the design decides in advance who checks what. Results are tested on examples from your own work before launch and checked after it. When the system is unsure or a source is missing, it says so and passes the case to a person.
Do we have to replace our current systems?
Not necessarily. Automation usually connects the tools you already use, such as your CRM, ERP, inbox or document storage. If a gap can only be closed with a new application, we'll explain why before proposing one.
Can we start with one process?
Yes. One process and a limited pilot is a sensible first step, and you decide on the next step after seeing the results.
Have a process in mind?
Describe what your team does by hand today and which systems are involved. We'll reply by email, usually with questions or a suggestion for a call.