AI integration
An AI system with no access to your systems is a demo. We build the typed tool contracts, scoped credentials, retry semantics and audit trails that let one read and change real state safely.
What is ai integration?
AI integration is the work of connecting a model-driven system to the software a business already runs — CRM, ERP, ticketing, data warehouse, internal APIs — so it can read real state and take real action. It is where most of the engineering time in an AI project goes, because every system has undocumented behaviour, and because giving an autonomous system write access safely requires scoped credentials, validated inputs and an audit trail rather than a shared admin key.
Why teams call us
Almost every engagement starts from one of these. If none of them sound familiar, you probably do not need us yet.
Integration is done quickly with an administrative account because it is faster, and the permissions are never narrowed afterwards. The result is an autonomous system that can reach far more than its task requires, with no per-action record of what it touched.
A single incident is usually enough to get the whole programme paused.
Model output is passed to an API without schema validation. Most of the time it conforms; occasionally it does not, and something slightly wrong is written into a system that other processes trust.
Data-quality incidents that surface weeks later and are expensive to unwind.
A multi-step process fails halfway. Some steps have committed, some have not, and there is no compensating action or human queue — so the case is silently abandoned in an inconsistent state.
Reconciliation work that costs more than the automation saved.
A core system has no modern API, so the project is scoped around it or abandoned — when a read replica, a nightly file exchange or an existing event stream would have worked.
The highest-value use cases get excluded for a solvable reason.
Scope
Every action an AI system can take is defined as a schema-validated function with explicit inputs, outputs and error cases, so the model's output is checked at the boundary rather than trusted.
Integrations against the systems you actually run — Salesforce, HubSpot, SAP, NetSuite, Zendesk, Jira, ServiceNow, Snowflake, Postgres — including the undocumented behaviour each one has.
Where no API exists, we work through read replicas, file exchange or event streams, and treat interface automation as a temporary bridge only.
Per-tool least-privilege credentials, enforced limits, and a decision log that lets an auditor reconstruct exactly what happened.
How it runs
Fixed checkpoints with a named deliverable at each one, so you can stop the engagement at any stage and still own something useful.
We inventory the systems involved, what access is available, what the real data looks like, and where the undocumented behaviour is. This is also where legacy systems get a viable access path rather than an exclusion.
Tool contracts defined and built against your non-production tenants, with validation, idempotency and error handling in place before any production credential exists.
Least-privilege credentials provisioned per tool, limits enforced in code, and audit logging wired to your log sink.
Staged enablement with reads first, writes behind approval gates, and monitoring on every integration point.
Results
Ask a team what an AI project consists of and you will hear about models, prompts and retrieval. Look at the commit history of a shipped system and the majority of the work is integration: reading state out of systems that were never designed to be read from programmatically, and writing back into systems that assume a human is doing it.
This is not a failure of planning. It is the nature of the work. Every enterprise system has behaviour that is not in its documentation — a field that is nominally optional and mandatory in practice, a rate limit that is lower than published, an endpoint that returns success while doing nothing. You find these by building against the system, which is why integration estimates that assume the documentation is accurate are always low.
The single most common finding when we audit an existing AI deployment is one credential with broad access, created during the prototype and never narrowed.
The fix is unglamorous. Each tool gets its own credential at the minimum access level that works. Reads are separated from writes. Access is scoped to specific objects, tables or endpoints rather than granted at the account level. Spend and volume limits live in code, outside anything the model can influence. And every action is logged with the context that produced it.
None of that is novel security practice. It is the ordinary practice, applied to a caller that happens to be probabilistic — which is exactly why it matters more here than usual.
“Our core system has no API” ends more AI use cases than it should. In order of preference, the paths that generally work:
The ordering matters because teams frequently start at four.
Next Step
A 30-minute technical call with an engineer who has shipped this before — not a sales qualification round. You leave with a feasibility read, a rough shape for the build, and an honest answer about whether it is worth doing at all.