STELA RPA Fundamentals turns a designed business procedure into a robot you can execute, test, and explain. Begin with one purchasing proposal, then introduce the data, decisions, and checks needed to process a batch. The course develops practical automation skills while keeping each operation linked to its input, result, and evidence. Learn the STELA workspace and build flows with objects, actions, variables, and conditions. Review Robot Maker proposals and use Computer Vision with attention to the identity of the object being selected. Read spreadsheet data, write tracking records, and reopen files to verify that the results were saved. You will also reconcile a batch and identify records that need further attention. Practice alternative paths, human intervention, duplicate checks, and decisions after an uncertain result. Introduce OCR by comparing extracted fields with the original document. Use STELA’s IA GPT capability to interpret text, then validate its output against allowed categories and business rules. Missing information stays unresolved until there is an appropriate decision; an AI response does not grant authorization. The final project applies the method to purchasing and clothing allocation, with test evidence, an operating guide, business acceptance, and individual review. Fillable workbooks, spreadsheet templates, and document samples support the activities. The course requires the process-design competencies from STELA – Introduction to RPA and access to the assigned practice environment. No programming knowledge is required.

– Getting started: prepare the practice resources and understand execution evidence, assessment, and corrections.
– From process design to STELA: connect the procedure, required data, controls, and expected business result.
– Your first robot with controls: build actions and conditions, review Robot Maker, and use Computer Vision with context.
– Variables and structured data: load complete records and prevent values from carrying over between operations.
– Spreadsheets: read rows, write results, reopen files, and respond to failed or blocked persistence.
– Batch processing: identify every record, define loop completion, and reconcile inputs with results.
– Alternative paths and human intervention: assign decisions, preserve evidence, and revalidate before resuming.
– Duplicates and uncertain results: query previous effects and define when a retry is justified.
– OCR: extract text, compare fields with the original, and handle ambiguous documents.
– AI interpretation with IA GPT: bound the task, evaluate actual responses, and reject invalid or unauthorized outputs.
– Robot testing and handover: execute a test matrix, perform regression checks, and obtain business acceptance.
– Final project: implement purchasing and clothing-allocation procedures, deliver evidence, and explain an individual variant.