Controlled system connections · AI Integration & Platform Engineering

Enterprise AI Integration

An AI integration is complete only when source-system authority, transaction state, reconciliation, recovery, and named support ownership survive every connection in the business path.

We connect an AI capability to enterprise identities, data, events, workflows, and transactions without opening a side channel around existing authority. Reconciliation, recovery, failure isolation, and audit evidence sit inside the business path.

A connected-system map, an interface and audit contract file, a reconciliation record, and authorization and recovery findings let your support teams trace a transaction and recover it after handoff.

Map the business pathView the application development service
Illustration of Enterprise AI Integration: a team connecting an AI capability to existing enterprise systems

Some of the 500+ brands we've worked with

See all references
  • DenizBank
  • Akakçe
  • Apsiyon
  • AVVA
  • DYO
  • Elele

Steps, gates, and who decides

How we work

We follow the business event from its first identity check to its final transaction state. Authority, ownership, and recovery have to remain intact at every handoff.

  1. Trace the transaction and its owners

    We follow the process across systems, identities, data classes, events, and transaction states. Each consequential step is tied to the team that owns it and the service level they support.

    AI assist
    Existing architecture documents give the model enough context to draft a first system-and-owner map, which each owner verifies.
    Human gate
    Is every consequential step tied to a system and business owner? Your system owners confirm which team owns each consequential step.

  2. Preserve source-system authority

    Before the AI capability connects, we define interface and event contracts, permission checks, orchestration, audit fields, reconciliation, and failure isolation. The connected path must enforce the authority of its source systems.

    AI assist
    The model compares proposed interface contracts with source-system permission rules and marks gaps for engineering review.
    Human gate
    Can the path enforce the same authority as the source systems? Your system owner approves the permission model before interfaces connect.

  3. Leave failed business states visible

    End-to-end, unauthorized-path, partial-failure, retry, reconciliation, and recovery cases run through the workflow. The test keeps going after the model responds and checks every downstream state.

    AI assist
    Unauthorized-access and partial-failure scenarios are model-generated from the mapped contracts, then engineers validate them before use.
    Human gate
    Does a failed step leave a detectable and recoverable business state? Your system owner reviews every reconciliation gap found during testing.

  4. Release in stages and assign support

    We open the path gradually, inspect unreconciled transactions and recovery evidence, and transfer each escalation to the team that owns the affected system or business state.

    AI assist
    The model uses staged-release evidence, failed states, and recovery cases to prepare a support runbook draft for the operating teams.
    Human gate
    Can support identify who owns a failed or incomplete path? Your support owner accepts escalation responsibility before go-live.

Named artifacts you keep

What you get

The handoff combines the connected architecture, its authority and transaction contracts, and evidence from the complete business path.

  • Architecture document

    Connected-system identity and transaction map

    The connected systems, identities, data classes, events, transaction states, owners, and failure boundaries.

  • Policy

    Interface, event, retry, and audit contract file

    Request, response, schema, event, retry, error, and audit expectations for every connection in the agreed path.

  • Matrix

    Permission, transaction, and reconciliation map

    Identities, roles, approvals, business side effects, transaction states, and the owners responsible for reconciliation.

  • Test evidence

    Authorization, recovery, and support handoff findings

    Authorization, contract, reliability, recovery, and staged-release results with the support and escalation instructions attached.

Scope and honest limits

When to bring us in

Bring us in when an AI capability must cross real identity, system, event, or transaction boundaries and the enterprise controls already in place must still govern every side effect.

A good fit when

  • The business process crosses systems with different identity models, so a failed transaction can lose its owner at the boundary.
  • Interface or event contracts disagree across teams, but nobody can say which request, retry, or audit behavior governs the connected path.
  • Authorization and recovery expectations exist, yet transaction and support owners have not accepted the same service-level path.
  • Systems, identities, data classes, and transaction states are known separately, but no map follows the business event across their boundaries.
  • Your interfaces and permissions connect, while orchestration, reconciliation, and failure isolation still leave gaps between source-system controls.
  • A model response passes, but end-to-end tests still expose unauthorized routes, partial failure, or downstream states that cannot recover.
  • The audit trail records a completed step, yet it cannot show which identity initiated, approved, and finished each consequential action.

Better handled as other work when

  • You want an AI route that bypasses a source-system authorization or approval control, but the connected path must preserve that authority.
  • Calling a transaction complete because the model responded while a downstream system remains inconsistent.
  • You need source-system ownership replaced, new data acquired, or every connected service operated, but those responsibilities require separate scope.

If one of these is closer to your situation, start here instead: View the application development service

Engineers who ship production AI

This is the part of Zeo that writes and ships code. Our senior engineers build agents, chatbots, and RAG pipelines, along with the automation and data work around them, and they keep operating those systems once they're live. We've worked with more than 500 brands since 2011.

Content we've produced on this topic

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Tools we use

Tools behind this work

  • Amazon Web Services

    the identity and event infrastructure the AI connection inherits, not bypasses

  • LiteLLM

    one consistent model interface across the connected workflows and transactions

  • Portkey

    the gateway layer producing the audit trail this integration is required to keep

  • Datadog

    watches for failures inside the integrated path before they reach the business transaction

  • Langfuse

    the trace a reconciliation or recovery investigation reads after a fault

  • Guardrails AI

    checks that AI output respects the authority of the identity that invoked it

Next step

Trace the transaction before connecting AI

Map the business process, system owners, contracts, and transaction boundary with us. The result covers authority, recovery, and support end to end.
Plan the system path

Before you decide

Questions we hear a lot

Bring the full path: the business process, identity and role model, interface and event contracts, data classifications, transaction owners, SLOs, and support model. The people responsible for authorization, downstream side effects, and recovery must be available to close any contract gap.