AI Integration & Infrastructure

RPA (robotic process automation) repeats clicks and keystrokes in existing screens, which suits stable, rule-based steps. AI workflow automation works through APIs and adds judgment steps: reading unstructured documents, classifying requests and routing exceptions. Many projects combine both, with RPA for older systems that have no API.

Good candidates repeat often, follow a recognizable pattern, start from a digital input such as a document, form, ticket or email, and have a clear definition of a correct result. Examples are invoice processing, claims triage, contract review, support routing and compliance screening. Processes that change every week or depend on case-by-case judgment are better handled with AI assistance for a person.

The AI Pilot Sprint takes one workflow to daily use in six weeks: access and baseline, build and testing on real cases, then live use with adjustments. In our experience, system access and approvals set the pace; larger scopes are split into separate waves.

Intelligent document processing (IDP) extracts structured data from documents such as invoices, contracts, clinical notes and claim forms using OCR and language models. Current IDP uses a language model with a fixed output schema, checked against reference data, which copes with layout changes that older template-based tools could not handle.

Every workflow has an explicit confidence threshold: outputs above it flow through, outputs below it go to a person with the AI's reasoning attached. Reviewer corrections are logged and used to tune the workflow. The threshold is set from accuracy measured on your own cases.

Yes. Inputs change, model providers release new versions and business rules move. Each workflow needs monitoring of error rates and review volume, an owner, and a test set that is run before any change. Remolda hands over a runbook and offers ongoing support as a separate monthly engagement.