The glue code between your systems
built and maintained for you
Every business ends up with a pile of glue code connecting a CRM to a spreadsheet to a payment provider to a messenger. It breaks quietly whenever one of them changes its API without warning. We build an agent that maintains that integration layer. It catches schema changes before they cause silent data loss, and retries failures the right way, instead of flooding a provider with requests.
Why glue code breaks quietly
Most businesses running more than two or three tools end up with integration code nobody fully owns. A script someone wrote two years ago pushes CRM leads into a spreadsheet. A webhook handler never got updated when the payment provider changed its payload format. A sync job silently stopped working three weeks ago and nobody noticed, because nothing threw an error loudly enough. Integration work is consistently reported as one of the least favorite and most error-prone parts of software maintenance. That is precisely because it depends on another company’s API staying stable, which it frequently does not.
The failure mode that hurts most is the quiet one. A field gets renamed on one side. The sync job keeps running but silently drops the new field. Three months later someone notices a CRM full of leads missing a phone number nobody remembers removing. Unlike a crash, a silent schema mismatch does not generate an alert. It generates a slowly growing pile of bad or missing data that erodes trust in the system faster than an outright outage would.
What the agent builds and maintains
Builds and documents the field mapping between your systems, translating one tool’s data shape into another’s. The mapping gets written down somewhere a human can actually read it, not buried in a script’s variable names.
Retries transient failures with backoff, telling a temporary network blip or rate limit apart from a genuine data problem. That way a retry storm never turns a small outage into a bigger one against the provider on the other end.
Detects schema changes before they cause silent data loss, comparing the shape of incoming data against what it expects. It alerts the moment a field disappears, renames, or changes type, instead of quietly dropping it.
Logs errors per record, so one malformed entry in a batch of five hundred does not block the other four hundred ninety nine. The one bad record is easy to find and fix.
Paces requests against each provider’s actual rate limits, keyed to that specific API’s documented limits, rather than a one-size-fits-all throttle. That avoids the account-level bans that come from hammering an external API.
Routes failed records to a replay queue a human can review and resubmit once the underlying issue is fixed, rather than losing them or silently retrying forever.
What stays with your team
The agent builds and maintains the sync. It does not decide the business rule when two systems disagree about a record. An example: which system wins when a customer’s address differs between a CRM and a shipping provider. That call stays with you. Any new integration involving payment data, authentication credentials or a provider not yet vetted for rate limits and terms of service goes through a human setup step first. A persistent schema mismatch that cannot be resolved automatically is handed to a person rather than guessed at.
How we keep integrations safe
A parallel-run period comes first: the new integration runs alongside your existing process, if one exists, and results are compared before fully cutting over. That way a mapping error surfaces in testing rather than production. Every sync and every failure is logged per record, with enough detail to debug without re-running the whole batch. Rate limits are set per provider based on their actual documented limits, never a guess, specifically to avoid account bans from retry storms. A kill switch stops the integration instantly, and a manual replay queue means a fixable failure is never silently lost.
Price and timeline
| Package | Price | Best for |
|---|---|---|
| Single automation | from $1,200 | One integration between two systems, with retry logic, schema monitoring and a replay queue |
| Department package | from $2,500 | Several integrations across your stack, built and documented together |
5 to 12 days, most of it spent on field mapping and a parallel-run period, so the first live sync has already been checked against real data.
Related
Underpins database reports and catalogue sync between stores, both of which depend on reliable integration glue underneath. Teams running uptime and error monitoring often extend it to watch the integrations themselves. Part of automation of everything digital and built the way we build AI agents for our own products. We have built this exact layer for our own and client systems, including CRM lead routing for a real estate agency. The factory ERP recovery on self-hosted infrastructure shows the same discipline on a much harder case.
Tell us which systems need to talk to each other and we will send back a fixed price and a plan for the first week: get in touch.
Tired of doing this by hand? We can take the whole routine off your team, not only this step: Routine takeover, from $400 →
FAQ
How much does an AI API integration agent cost?
A single integration between two systems starts from $1,200. A department package starts from $2,500: several integrations at once, a CRM plus a payment provider plus a messenger, for instance. The exact price depends on how many systems and how much custom field mapping is involved.
How long does it take to go live?
5 to 12 days. That covers mapping the fields between your systems and building retry and validation logic. We then run the integration in parallel with your existing process, if you have one, before fully switching over.
Which tools does it connect to?
Any system with a REST API, webhook or documented export. That includes CRMs like amoCRM, HubSpot or KeyCRM, payment and delivery providers, and Shopify and other marketplaces. It also covers Google Sheets, Slack and Telegram, plus legacy systems through screen automation when there is no API at all.
What if a sync fails partway through?
Failures are logged per record, not per batch, so one bad record does not block the rest. Failed records route to a replay queue a human reviews, rather than disappearing silently. Retries use backoff, so a temporary outage does not turn into a request storm against the other system.
Is our data safe moving between systems?
Data stays within the systems and connections you authorize. Credentials and API keys are yours and revocable at any time, and we do not retain data in transit outside the sync session. Sensitive fields can be excluded from logging entirely.