Closing the Last-Mile Gap

Intro

Enterprises have invested heavily in orchestration --- agents, workflow engines, integration platforms, and LLM-based copilots designed to coordinate work across systems. Yet most of these initiatives underperform for a simple reason: the data they orchestrate still originates in unstructured documents that no system reliably reads. Orchestration is only as good as its inputs.

This white paper examines that last-mile gap and makes the case for document intelligence as foundational infrastructure rather than a point solution.

Key themes include

• The orchestration paradox: Sophisticated agent architectures sit on top of brittle, template-dependent extraction, so automation stops at the first non-standard document.
• Why LLMs alone don't close the gap: General-purpose models lack layout understanding, per-field confidence, and traceability --- the properties enterprise workflows need to act autonomously.
• Document intelligence as an orchestration layer: Classification, extraction, validation, and confidence scoring that produce machine-actionable data any downstream agent or system can trust.
• Governance for autonomous action: Bounded autonomy, audit trails, and escalation rules that make straight-through processing defensible in regulated environments.
• An architecture and adoption pattern: How to sequence document intelligence into an existing orchestration stack without re-platforming.

Outcome

Organizations that treat document understanding as infrastructure --- not an add-on --- convert stalled orchestration pilots into production automation with measurable throughput, accuracy, and cost gains.