AI integration services
AI integration services sound like the model is the hard part. It is not, and it has not been for a while. Connecting one to a business that already runs on five systems comes down to four unglamorous layers, and a project fails in one of them almost every time.
The four layers, and what fails in each
- Access. Who is the machine allowed to be? It needs credentials to your systems, and it must not be given a person's. A service account with the narrowest rights that still lets it do the job, revocable in one place. Most stalls happen here, waiting on a permission nobody owns.
- Data. What is it allowed to see, and is it true? A model reads exactly what it is handed. If two systems disagree about a record, it will pick one and act with total confidence. The join comes before the model, always.
- Action. What can it change, and inside what limits? Reading is safe and cheap. Writing is where the value and the risk both are. Every write needs a limit — a spend cap, a margin floor, a volume ceiling — and a stop that names its reason.
- Audit. Can you show what happened? Every decision recorded with the inputs it used, kept long enough to settle an argument three months later. Without this you cannot debug it, and you cannot defend it.
What a working integration looks like on day one
- One system, not five. The second one is easy once the first is honest.
- Read-only for the first week, with its answers compared against what a person would have done.
- One write, inside a limit, with a named reason when it stops.
- A log a person can read without a developer.
Questions worth asking before anyone connects anything
- If this leaks, what is the worst single record that gets out? Answer that before choosing where the model runs.
- Which system is the source of truth when two disagree? If the answer is “it depends”, that is the first piece of work.
- Who gets told when it stops, and how fast? An alert nobody owns is decoration.
- What is the monthly running cost at real volume, and what makes it grow?
Too early for you if
- Nobody can say which system holds the true version of a record. Fix that first; it pays off on its own.
- The work you want automated happens a few times a month.
- There is no one on your side who can say an answer is wrong. Without that person, nobody will notice when it drifts.
Layers already wired together
- Data platform — the data layer done properly: every system into one clean store, one true record per product, and an AI layer on top that answers from it.
- Customer-chat assistant — the action layer with a limit on it: one AI writes the reply and a second, independent one signs it off before a buyer sees it.
- Personal AI assistant — the access layer in daily use — voice notes transcribed, research done, client history filed where it can be searched.
- Systems watchdog — the audit layer's blunt cousin: it notices the moment any of it stops and says so the same day.
Honest answers
What do AI integration services actually involve?
Four layers: giving the machine its own narrow credentials, handing it data that is true rather than contradictory, letting it act inside limits you set, and recording what it did well enough to settle an argument later. The model itself is rarely the hard part.
How long does an AI integration take?
The first working connection is usually a couple of weeks, and it is deliberately one system rather than five. What lengthens it is never the model — it is waiting on a permission nobody owns, or discovering two systems disagree about the same record.
Is my data safe if a model reads it?
That depends on choices made before anything connects: where the model runs, what it is allowed to see, and whether anything is retained. The useful question is not whether AI is safe in general but what the single worst record is that could leak, and the answer shapes the design.
Can it write to my systems, not just read?
Yes, inside limits you set — a spend cap, a margin floor, a volume ceiling — and it stops and names the reason outside them. Reading first for a week, then one write, is the order that keeps this boring.
What if my systems have no API?
Common, and usually solvable — an export on a schedule, a database read, or a small service in front of the old system. It is worth pricing that piece separately rather than hiding it inside the estimate.
Do we have to send data to a model provider at all?
Not always. Some of this runs on a model you host yourself, and the trade is cost and quality against where the data goes. It is a decision to make with the worst single record in mind, and it belongs at the start of the project rather than at the review.
Tell me what has to talk to what
Two lines: which systems hold your data, and what you want the AI to actually do with it. I will tell you which of the four layers is your real problem and what the first connection should be. The first conversation is an hour and it is free.
Book that hour →