Your questions, answered.

Start with work that takes time, happens repeatedly, and has a clear outcome: handling inquiries, preparing estimates, finding information, organizing documents, or following up with customers. We look at the process and its cost before deciding whether AI, straightforward automation, or a simpler change makes sense.

No. Bring a process that feels slow, difficult to manage, or dependent on someone keeping everything in their head. A few real examples of how the work moves today are more useful than a technical specification.

We discuss the work you want to improve, the systems you already use, and the outcome that would make a project worthwhile. The aim is to identify a sensible next step and whether we can help. You do not need to share sensitive records or commit to a build to have that conversation.

Yes. A small business might need help with one inbox or one handoff; a larger organization may need several systems connected with more formal access, review, and rollout controls. We shape the scope around the work, the risk, and the people who will use it.

Not necessarily. We first look at how your current systems can connect and what access they allow. Some offer useful integrations; others need a different approach or make a workflow impractical. We work through those dependencies before agreeing the build.

Automation follows defined steps, such as sending a reminder when a job is complete. AI can help interpret less structured information, such as a technician’s note or a customer’s message. A useful workflow often combines both, with people reviewing the parts that need judgment.

Timing depends on the number of systems involved, data readiness, access, testing, and approval requirements. A focused workflow is very different from a rollout across several teams. We agree a delivery plan after understanding those details rather than promising the same timeline for every project.

We agree what to measure before a pilot: time spent on a task, response speed, missing information, rework, or the number of items needing manual review. We compare the result with the existing process and include the running costs, so the decision to continue is based on something observable.

We aim to make the day-to-day workflow usable by the people doing the work. Training, documentation, and handover should match their responsibilities. If the system requires someone to manage access, review exceptions, or maintain a connection, we identify that role during scoping.

Before connecting project data, we agree what is needed, which services will process it, who can access it, and the retention requirements. Provider settings and contractual terms matter. Please use a high-level business summary in the website form; we can agree a suitable way to exchange sensitive information later.

That depends on the provider, product, account settings, and terms chosen for the project. We review those requirements before selecting a setup. A no-training or data-location requirement needs to be made explicit and checked against the actual services involved.

Yes. AI can miss context, produce incorrect information, or sound confident when it is wrong. We define what the system may do, what needs human approval, and what happens when information is missing. Testing should cover exceptions and failures as well as the straightforward cases.

Yes. A limited pilot can test a workflow with a smaller dataset, a defined group, or draft-only outputs. We agree the success criteria and review points before expanding its use or granting broader permissions.

Pricing follows the agreed scope: the process, connections, data preparation, testing, training, and support required. We explain project fees and expected third-party subscriptions or usage charges separately. There is no need to choose a package before we understand the problem.

Your agreement should state what is handed over, what you own, and what is licensed. Third-party software and AI models remain subject to their providers’ terms. We also agree account access, documentation, and ongoing dependencies so you understand how to operate the system after handover.

We do not promise a fixed saving, revenue increase, or error-free AI output. Our assessment offer provides a fee refund if we cannot identify at least five high-ROI opportunities during that engagement. Confirm the offer’s criteria and how it applies in your written assessment agreement before starting.

We agree who owns the workflow, how issues are reported, and which maintenance or monitoring tasks need attention. Ongoing support can be scoped separately; it is not assumed to be included indefinitely. Changes to models, connected systems, or your process may require re-testing.