Skip to content
Docs

RPA & Document Automation

A large share of enterprise work is documents arriving somewhere and a person moving data from them into a system. Interplay treats that as a flow: ingest, classify, extract, validate, and post — with a human in the loop where the risk warrants one.

What can Interplay do with documents?

Section titled “What can Interplay do with documents?”
  • Ingest from file drops, network shares, mail streams, object storage, and HTTP uploads.
  • OCR and extract text, tables, and fields from scanned documents and PDFs, including image-only pages with no text layer.
  • Classify documents by type using a built-in trainer — label a sample set, train, and the flow routes each incoming document by its predicted class.
  • Parse and transform the extracted content into the structure a downstream system expects.
  • Ground an LLM in the extracted content for summarization, question answering, or field inference that rules cannot express — see AI Agents & LLM Orchestration.
  • Route for approval before anything is written to a system of record.

An OCR-optimized deployment variant is available for workloads that need the full document-AI stack on the same host. See Deployment.

What does RPA mean in Interplay?

Section titled “What does RPA mean in Interplay?”

Robotic process automation nodes let a flow drive systems that expose no usable API — the class of legacy applications, portals, and terminals that would otherwise require a person to click through them. RPA steps sit alongside ordinary nodes on the canvas, so a single flow can call a modern REST API, drive a legacy UI, and invoke an AI model in sequence, with the same logging and error handling across all three.

What are the typical use cases?

Section titled “What are the typical use cases?”
  • Invoice and purchase-order processing — extract line items, match against the ERP, flag exceptions for review.
  • Claims and onboarding packets — classify a mixed bundle of documents, extract per-type fields, and route.
  • Records digitization — bulk OCR of archives into a searchable, vector-indexed store.
  • Compliance evidence collection — pull artifacts from multiple systems on a schedule and assemble a report.
  • Legacy-system bridging — expose an RPA-driven process as a modern HTTP endpoint other applications can call.

Sources and references

Section titled “Sources and references”