A renamed field, an expired token, an endpoint retired with three months notice nobody read. We watch the systems on both sides of your AI and fix the joins before they take the whole thing down.
In short: We keep the connections around your AI healthy — database schema changes, expiring credentials, deprecated endpoints, shifted rate limits, fields that got renamed. Every integration is made to report that it ran, so a job that stopped silently cannot pass for one that worked.
The model is rarely what broke
When a client tells us the AI stopped working, the model is almost never the culprit. Something either side of it moved. A CRM added a required field. A vendor rotated an API key. A data team renamed a column in a table nobody realized was feeding the pipeline. The model does exactly what it always did, on inputs that are now subtly or entirely wrong, and the visible symptom is bad output. This is the least glamorous part of maintaining an AI system and it accounts for more incidents than everything else combined.
Schema and field changes
Columns renamed, types altered, a field that now arrives null. We validate the shape of incoming data on every run so a change is caught at the boundary, not three steps later.
Credentials and tokens
Keys expire, service accounts get disabled, certificates lapse. We track expiry dates and warn ahead of time instead of finding out at 3am on a public holiday.
Deprecated endpoints and versions
Vendors retire API versions and announce it in a changelog nobody subscribes to. We follow the ones your system depends on and plan the move before the shutoff date.
Rate limits and quotas
Limits get tightened, plans get changed, your volume grows. Throttled requests often fail quietly, so we watch for them and set retry and backoff behavior that degrades sensibly.
Silence must never look like success
Here is the rule we apply to every connection we touch. If a job runs, it says so. If it should have run and did not, something notices and complains. Most integration disasters we have been called in to clean up share the same shape: a scheduled sync stopped weeks ago, no error was raised because nothing ran to raise one, and the absence of complaints was read as everything being fine.
The fix is not complicated. Each integration writes a heartbeat when it completes, with a record count. A watcher expects that heartbeat inside a window and raises an alarm when it does not arrive. Record counts that fall off a cliff get flagged too, because a sync that returns four rows instead of forty thousand technically succeeded. None of this is clever engineering. It is just the difference between knowing and assuming.
Both sides of the connection
Upkeep means watching what feeds the AI and what consumes it. On the input side that is databases, warehouses, file drops, CRM and helpdesk APIs, whatever supplies the context. On the output side it is the systems that receive a decision and act on it — the ticket that gets routed, the record that gets updated, the message that gets sent. Breakage on the output side is easier to miss, because the AI still looks healthy while its work quietly stops arriving anywhere.
We keep a written inventory of every connection: what it talks to, which credentials it uses, when they expire, who owns the system at the other end, and what happens when it fails. That last column is the one most teams have never filled in. Knowing whether a failure should stop the pipeline, retry, or fall back to a human is a decision worth making calmly in advance rather than during an incident.
What this looks like month to month
Mostly nothing dramatic, which is the point. Credentials get rotated before they lapse. Vendor changelogs get read. A deprecated endpoint gets migrated in a quiet week rather than an urgent one. Occasionally a schema change comes through and the validation catches it the same day, which turns what would have been a week of confused debugging into a twenty minute fix. Over a year the value shows up as incidents that never happened, which is admittedly a hard thing to put on a slide.
What we won’t do
Take responsibility for systems we cannot see
If a vendor gives no changelog, no test environment, and no notice of breaking changes, we can react quickly but we cannot prevent it. We will name those connections as fragile up front rather than implying we have them covered.
Paper over an integration that should be rebuilt
Some connections fail every few weeks because they were wired together in a hurry. Patching those indefinitely bills more hours and helps you less. We will say when a rebuild is cheaper than another year of repairs.
Add retries that hide a real problem
Retry logic is useful for genuinely transient faults. Wrapping a persistent failure in enough retries that it stops appearing in the logs is not a fix, and it delays the day someone looks at the actual cause.
Do you know every system your AI depends on?
Most teams find one or two they had forgotten about. Send us what you have and we will map the connections and tell you which ones look brittle.
Because an AI system sits in the middle of other people’s software, and other people change their software. A renamed column, a rotated key, a retired API version – none of those are unusual events, and any one of them can leave the model running on inputs that are wrong. The model keeps answering. The answers just stop being useful.
What does a heartbeat check actually do?
Each integration writes a short record when it finishes, including how many items it processed. A watcher expects that record inside an expected window. If it does not arrive, an alert fires. It also flags counts that drop sharply, since a sync returning four rows instead of forty thousand has technically succeeded while delivering almost nothing.
Can you look after integrations built by someone else?
Yes, and that is most of the work we get. We start by mapping what connects to what, which credentials each connection uses, when they expire, and what happens on failure. That inventory usually turns up a couple of dependencies nobody remembered. Then we add validation and heartbeats to whatever is missing them.
How quickly do you respond when something breaks?
Response times are agreed as part of the arrangement rather than promised in the abstract, because what is appropriate depends on what the system does. A pipeline that feeds a nightly report and one that routes live customer messages deserve different commitments. We would rather set a target we can hold to than quote a number that sounds impressive.
