How long until we see something?

Two weeks for the diagnostic. Six weeks for a working pilot. Twelve weeks for a live production system. If we can't hit those, we'll tell you upfront.

Yes. All of it. Your repository, your deployment, your servers. We don't host your system and we don't lock you into our tooling.

What if AI isn't the right answer?

We tell you. The diagnostic is designed to catch this. About one in five diagnostics ends with us recommending the client not automate, or to fix something else first. We'd rather lose the build than sell you something that won't work.

Are you a consultancy or a software house?

Both. We started as a software house in 2017 and we've delivered 40+ production systems. We added the strategy and adoption work because clients were asking us to. The two halves work because we don't recommend things we couldn't build ourselves.

Can you work alongside our existing IT team?

Usually yes. Most of our engagements involve someone in-house who'll own the system after we leave. We design the handover from day one.

What about data protection and security?

UK data residency by default. ISO 27001 aligned. We've delivered into financial services, legal, and healthcare. We can share our security pack under NDA.

Common mistakes in AI adoption | Appoly Intelligence

After running pilots and rollouts for UK businesses, we’ve seen the same patterns repeat. Here are the mistakes that derail AI adoption — and how to avoid them.

1. Starting with the tool, not the problem

ChatGPT is impressive. That doesn’t mean your business needs it. The right starting point is a process that costs you time or money — then finding the tool that fixes it.

2. Expecting magic from bad data

AI systems need clean, structured inputs. If your data’s a mess, the AI will be too. Sometimes the right answer is “fix the data first” — and we’ll tell you if that’s the case.

3. Skipping the pilot

A full rollout without a tested pilot is a bet, not a plan. Pilots surface the real problems: integration gaps, user resistance, edge cases. Better to find them with ten users than a hundred.

4. Not planning for handover

The best systems are the ones your team can run without us. If the knowledge leaves with the consultant, you don’t have a system — you have a dependency.

5. Ignoring the people

AI changes how people work. If you don’t bring them along — show them what’s in it for them, train them properly, listen to their concerns — they’ll work around the system instead of with it.

What works instead

Start with a diagnostic. Know what you’re solving for. Run a pilot. Plan the handover from day one. And treat the people side as seriously as the technology.

Talk to us about getting it right.