AI integration in Cyprus
Putting AI inside the systems you already run, rather than beside them as another tab nobody opens.
The pilot usually works. What fails is the part after it: getting the model into the CRM, the ERP, the support desk or the internal tool where the work actually happens, with the permissions, the fallback and the audit trail the rest of your software already has.
Nicosia-based. Your systems, your data boundary, your access rules.
The pilot worked. It is still a pilot.
Almost every company we meet has proved that the model can do the thing. What has not been solved is everything between that proof and a colleague using it without being told to.
That gap is not a modelling problem. It is permissions, identity, retrieval over your real data, latency, cost per call, what happens when the provider is down, and who is accountable when the answer is wrong. Those are integration problems, and they are the reason so many pilots never become systems.
This is not the same as connecting two systems
We also do conventional system integration, and the two get confused constantly. The distinction is worth stating plainly, because the risks are different.
- 01
System integration moves data between systems
APIs and webhooks so the CRM and the accounting tool stop disagreeing. Deterministic, testable, and either working or not.
- 02
AI integration puts a model inside one of them
The model becomes part of a workflow your team already uses. That brings problems conventional integration never has: a component that is probabilistic, priced per call, occasionally unavailable, and capable of being confidently wrong.
- 03
Which is why the engineering sits around the model
Retrieval so the answer is grounded in your data. Permissions so it cannot surface what the user may not see. A fallback for when the provider is down. Cost controls. Evaluation. The model is the easy part.
What integration actually involves
Roughly the same six pieces every time, and the failure is almost always in one of the last three.
- 01
The surface
Where it appears: inside the CRM record, in the support desk, in the internal tool, as an endpoint your own software calls. Not a separate app people have to remember.
- 02
Retrieval over your data
The model answers from your documents, records and history rather than from general knowledge, and it cites what it used so an answer can be checked.
- 03
Permissions that match the system it lives in
The single most common serious defect we find. An assistant that can read everything will eventually show somebody a salary, a contract or another client's file. Access has to be enforced at retrieval, not asked for in a prompt.
- 04
Fallback and degradation
Providers have outages and rate limits. The workflow needs a defined behaviour when the model is unavailable, and it should not be a spinner that never resolves.
- 05
Cost and latency control
Per-call pricing turns into a real line item at real volume. Caching, model selection per task and hard ceilings, decided before launch rather than after the first invoice.
- 06
Evaluation you can rerun
A fixed set of cases with known good answers, run on every change. Without it, nobody can tell an improvement from a regression, and every prompt edit is a guess.
What we will not do
Two of these come up in most first conversations.
- 01
Bolt an assistant onto a system nobody trusts
If the underlying data is wrong or stale, an AI layer makes wrong answers faster and more convincing. Fix the data first; we would rather do that work than the demo.
- 02
Ship without permissions
Not negotiable, and not a phase two. An assistant that ignores access rules is a data breach with a friendly interface.
- 03
Replace a working integration with a model
If two systems already exchange data reliably over an API, putting a model in that path adds cost and uncertainty for nothing.
We run this ourselves
Pileform is not an AI feature bolted onto a product. It is a product with a model inside it, with the retrieval, the permissions, the confidence gate and the evaluation set that implies, and we operate it in production across 55 VAT jurisdictions. Everything on this page is something we have had to get right for ourselves.
Related
Questions we get asked
What is the difference between AI integration and system integration?
System integration connects two systems so data moves between them automatically, and it is deterministic. AI integration puts a model inside a system your team already uses, which introduces problems conventional integration never has: a component that is probabilistic, priced per call, sometimes unavailable, and capable of being confidently wrong. We do both, and we will tell you which one your problem actually is.
Can you work with the systems we already have?
That is the whole job. We integrate with what you run rather than proposing a replacement, and if a system has no usable API we will say so early, because that changes the approach and the cost.
How do you stop it from showing people things they should not see?
Access is enforced at retrieval, so the model is never given content the current user cannot already open. Asking a model in a prompt not to reveal something is not access control, and treating it as such is the most serious defect we routinely find in existing deployments.
What does it cost to run, not to build?
Model calls are priced per use, so running cost scales with volume in a way traditional software does not. We model that before building, then control it with caching, choosing a smaller model where a smaller model suffices, and hard ceilings. You get an estimate you can plan against rather than a surprise.
What happens if the AI provider goes down?
The workflow degrades to a defined behaviour we agree in advance, which usually means falling back to the manual path with a clear message rather than failing silently. We also avoid designs where a provider outage takes a core business process offline entirely.
Do you hand it over, or do we depend on you?
We hand over. The code, the evaluation set, the prompts under version control and the runbook are yours, and the handover is part of the engagement rather than an upsell. Our own product is the reason we take this seriously: we know what it costs to inherit a system nobody documented.
Start with the system, not the model
Tell us where the work happens today. The integration questions are the ones that decide whether this becomes a system or stays a pilot.