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

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  • DenizBank
  • Akakçe
  • Apsiyon
  • AVVA
  • DYO
  • Elele
  • Amazon
  • BMW
  • Shell
  • Hyundai
  • PepsiCo
  • Red Bull
  • Decathlon
  • MediaMarkt
  • Bayer
  • Sanofi
  • EY
  • KPMG
  • GE
  • 3M
  • Domino’s
  • Lexus
  • Trendyol
  • Hepsiburada

The run order, and who signs each step off

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. 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. 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. 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. 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

Which inputs should system owners provide?

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.

How can AI agents assist with the integration work?

They compare interface contracts, extract schema differences, prepare test cases, and organize traceable evidence from approved logs and documents. System and transaction owners verify the contracts and interpret the business state, then decide whether permissions, reconciliation, recovery, or release conditions change.

Which readiness signals show the full path is working?

Interface contract success, identity propagation failures, unreconciled transaction count, and end-to-end recovery time. We also test whether the AI path can bypass authorization, whether partial failure leaves systems in different states, and whether ownership vanishes at a boundary. Each finding goes back to a named owner.

Does enterprise integration remove operating risk?

No. Systems, contracts, identities, and business processes keep changing. This engagement provides controls, recovery evidence, and named support ownership for the agreed path. It does not make every connected service permanently reliable or compliant.