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, and it breaks quietly whenever one of them changes its API without warning. We build an agent that maintains that integration layer, catches schema changes before they cause silent data loss, and retries failures the right way instead of flooding a provider with requests.
The process today
Most businesses running more than two or three tools end up with integration code nobody fully owns: a script someone wrote two years ago to push CRM leads into a spreadsheet, a webhook handler that was never updated when the payment provider changed its payload format, a sync job that 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, 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, a sync job keeps running but silently drops the new field, and 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 does
Builds and documents the field mapping between your systems, translating one tool’s data shape into another’s with the mapping written down somewhere a human can actually read it, not buried in a script’s variable names.
Retries transient failures with backoff, distinguishing a temporary network blip or rate limit from a genuine data problem, so 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 and alerting 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, and 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, to avoid 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 humans
The agent builds and maintains the sync, it does not decide what the business rule should be when two systems disagree about a record, for example which system wins when a customer’s address differs between a CRM and a shipping provider. 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.
Guards
A parallel-run period where the new integration runs alongside your existing process (if one exists) and results are compared before fully cutting over, so 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 Bali real estate agency and the factory ERP recovery on self-hosted infrastructure.
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 just 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 covering several integrations, a CRM, a payment provider and a messenger, for instance, starts from $2,500, with the exact price depending on how many systems and how much custom field mapping is involved.
How long does it take to go live?
5 to 12 days: mapping the fields between your systems, building retry and validation logic, and running the integration in parallel with your existing process (if any) before fully switching over.
Which tools does it connect to?
Any system with a REST API, webhook or documented export: CRMs (amoCRM, HubSpot, KeyCRM), payment and delivery providers, Shopify and marketplaces, Google Sheets, Slack and Telegram, and 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, and 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.