How do we put AI inside the tools our teams already use?
AI integration and automation services
AI in a separate tab does not change how work gets done. We build AI and automation into the systems your teams already run, such as NetSuite, Xero, Slack, Notion and your HR and identity feeds, so the work happens in the ticket, the ledger or the record. It is for teams whose AI tools sit unused in another tab, or whose automated writes leave no audit trail. Every change that writes to your systems can be checked before it runs, undone if it goes wrong and traced afterwards, and it never has more access than the person it acts for. The first step is a 30-minute call.
Integration is the product
People will not switch into a chat window for a task their existing system already half-handles.
The value appears when the capability shows up inside the workflow: in the ticket, the ledger, the channel, the record. That makes integration the deliverable rather than the plumbing behind it.
If that work runs today on RPA bots driving the screens, our guide to RPA vs AI automation sets out when to keep a bot, extend it with an AI step or replace it with an integration.
Before building anything on an ERP or CRM, check what its vendor already ships. Our guide to AI for ERP and CRM sets the built-in AI of Salesforce, Microsoft, SAP and NetSuite against a layer of your own, with what each vendor charges and allows.
If you are still choosing which process to automate, our guide to AI workflow automation scores candidates on value, feasibility and risk and sets out which to build first.
The unglamorous parts that decide whether it survives
Idempotency
A retry must not double-write. Systems fail midway more often than they fail cleanly, and a non-idempotent sync turns a transient error into a data problem.
Dry-run modes
Every destructive operation should be inspectable before it lands, so a change can be reviewed rather than trusted.
Rollback paths
A defined way back from a bad run, decided before the bad run rather than during it.
Audit logs
What changed, when, on whose behalf, and why: a by-product of running rather than a compliance project afterwards.
Rate limits and backoff
Enterprise APIs throttle. Handling that properly is the difference between a sync that completes and one that half-completes nightly.
Identity and permissions
Anything that reads or writes on a user's behalf inherits that user's permissions, which means identity is part of the integration rather than a layer above it. SCIM provisioning, role mapping, and de-provisioning all have to work, including the unhappy paths.
Getting this wrong is how an automation ends up with more access than any human in the organisation.
The same questions decide when a no-code automation has reached its limits. Our guide to the signs a workflow has outgrown Zapier sets out what to try inside Zapier first, and when the risky part should move into an integration like the ones on this page.
The data an integration hands to an AI system needs the same scrutiny, and our guide to data strategy for AI sets out what to check on access, quality, ownership and personal data before anything is built.
Where the system is a general ledger at a company that reports to the SEC, the same identity rules are also Sarbanes-Oxley controls. Our guide to SOX controls for AI in the finance close sets out the change, access, evidence and review controls to put around an AI step before it runs in a live close.
What you get
- Integration pipelines with idempotent writes
- Dry-run and rollback tooling
- Audit logging and run observability
- Identity and permission mapping, including de-provisioning
- Scheduling with predictable, monitored runs
Start here if
- AI capability exists but nobody uses it because it is in another tool
- A sync fails partway and leaves inconsistent state
- No audit trail for automated writes
- Access granted to an automation that nobody reviews
Frequently asked questions
Can you work with our existing integration platform?
Yes. Where a workflow automation platform already handles the orchestration, we use it and add the missing guarantees around it. Custom middleware is worth building when the guarantees matter more than the convenience: typically idempotency, audit and permission handling that low-code tools do not express well.
How do you handle systems with no usable API?
Scheduled exports, file drops and database-level integration all work when an API is absent, provided the same guarantees hold: idempotency, dry runs and audit. Screen-scraping a system of record is where we advise against automating at all.