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KI-Integrationsdienste

Sie müssen nicht alles neu aufbauen. Wir integrieren KI in Ihre bestehenden Systeme.

Integrationsdienste

API-Integration

Verbinden Sie KI-Dienste über saubere, gut dokumentierte APIs mit Ihren Anwendungen.

Modernisierung bestehender Systeme

Fügen Sie KI-Funktionen zu älteren Systemen hinzu, ohne alles neu zu schreiben.

Workflow-Automatisierung

Automatisieren Sie wiederkehrende Aufgaben mit intelligenten Pipelines.

Datenpipeline-Einrichtung

Bauen Sie die Dateninfrastruktur auf, die KI braucht - Erfassung, Bereinigung, Speicherung und Echtzeitverarbeitung.

Wie wir integrieren

1

Bestehende Systeme prüfen

Ihre aktuelle Architektur und Datenflüsse verstehen.

2

Integrationspunkte entwerfen

Identifizieren, wo KI mit minimaler Störung Mehrwert schafft.

3

Inkrementell bauen & testen

Stufenweise mit gründlichen Tests bei jedem Schritt ausrollen.

4

Überwachen & optimieren

Kontinuierliches Monitoring stellt sicher, dass nach dem Launch alles reibungslos läuft.Kontinuierliche Überwachung stellt sicher, dass nach dem Launch alles reibungslos läuft.

Typischer Zeitrahmen

4-12 Wochen je nach Systemkomplexität.

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What does it take to integrate AI into systems already in production?

Integrating AI into a running system is mostly not a modelling problem. It is a question of where inference runs, what latency the workflow tolerates, which system the output lands in, what happens when the service is unavailable, and who is paged when it breaks. A model that answers correctly in eight seconds is useless at a workstation and fine in a nightly batch, and nothing about the model tells you which case you are in.

The work therefore starts from the target system rather than the model: its interfaces, its release process, its security review, and the person who owns it. Most integrations that fail did so because that person was not involved until the end.

Adoption is the other half. If a human has to copy a result from one screen into another, usage decays to zero regardless of accuracy, so the output has to arrive where the work already happens.

Für wen das ist

  • Companies with a chosen model or vendor and no path into their stack.
  • Brownfield estates where the target system predates the API era.
  • Teams whose pilot worked in isolation and stalled at deployment.
  • Businesses needing AI output inside an ERP, MES, CRM or case system.

What we need from you

  • Access to the target system and a test environment.
  • Whoever owns that system, involved from the first week.
  • The real latency and volume requirement, taken from the workflow.
  • Your security review process, started early rather than late.

What you get back

  • A working integration into the system people already use.
  • A defined fallback for when the model is unavailable or unsure.
  • Monitoring and alerting tied to the workflow, not just uptime.
  • A runbook naming who does what when it misbehaves.
  • Load and latency evidence against the real requirement.

What a typical engagement looks like

Inference into an ERP or MES

Wiring model output into the system of record so an operator sees it in place, with the fallback path defined before go-live rather than discovered during an outage.

Replacing a manual handoff

Where a person currently moves data between two systems, the integration removes the handoff entirely, which is usually where the measurable saving actually comes from.

Vendor model, your environment

Deploying a third-party model inside your boundary for residency or latency reasons, including the security review and the operational handover.

Regulatory and data constraints

Where inference runs is a compliance decision as much as an engineering one. Data residency, the acceptable vendor list and the GDPR position on transfers all constrain the architecture, and retrofitting residency onto a system built against a foreign-hosted API generally means rebuilding it.

Security review is routinely the longest pole and is entirely predictable. Starting it in week one rather than at go-live is the single change that most reliably shortens an integration timeline.

Frequently asked questions

Agents across existing systems

Where a workflow spans several systems and breaks on exceptions, a goal-driven agent copes better than a fixed integration script. It also needs different guardrails.

How Hermes Agents optimise business operations

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