AI Orchestration.
The discipline of running multiple AI components together reliably — choosing which model handles which step, sequencing them, handling failures, monitoring quality. The "ops" layer beneath the visible AI features.
In plain English
AI orchestration is the discipline of running multiple AI components together reliably in production. The visible part of an AI feature is usually a single LLM call. The orchestration is everything around it: which model to use, how to construct the prompt, how to validate the response, what to do on failure, how to monitor quality over time.
Good orchestration matters because most real AI applications involve multiple steps. Invoice matching: extract structured data → validate against schema → match to PO → check tolerances → route exceptions → post to ERP. Each step has its own model choice, error modes, and retry logic. Without orchestration, the whole thing is fragile; with proper orchestration, it runs reliably enough to leave unsupervised over weekends.
The orchestration stack typically includes: a routing layer (different models for different tasks), a validation layer (does the output match the expected schema?), a retry layer (handle transient failures cleanly), a fallback layer (when the AI is uncertain, escalate to humans), a logging layer (every decision auditable), and a monitoring layer (accuracy drifts get caught).
For SMB deployment, orchestration is what separates "we tried ChatGPT and it sort of worked" from "this has been in production for six months reliably." It's also what costs more to build than the AI itself — typically 70% of build time is orchestration; 30% is the AI bits everyone talks about.
Real examples
What this looks like in practice.
- Document processing pipeline: route to vision model → validate schema → escalate uncertain extractions → post clean ones.
- Enquiry triage: classify → check sensitive flags → route → draft reply → human queue or auto-send based on confidence.
- Quote generation: extract requirements → apply pricing rules → validate against margin floor → draft document → human review queue.
- Internal AI assistant: classify query → retrieve from right system → generate response → log interaction → flag uncertain answers.
See in action
Where we deliver this for clients.
Related terms
Adjacent concepts.
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