Client context
An operations team processing a steady stream of inbound documents by hand. Details are anonymized at the client's request.
Case study
Document processing automation for an operations team
An anonymized case of replacing manual document handling with an AI workflow that extracts, validates and routes documents — with people verifying the exceptions.
The business problem
Documents were read and re-keyed manually, occasionally misfiled, and slow to move through approval at volume.
The process before Mindstructs
Each document was identified, its key fields typed into a system, filed, and routed for approval — repetitive and error-prone.
Discovery and requirements
We mapped the document types, the fields that mattered, and the rules for validation and routing, and captured them as structured requirements and task cards.
The solution we designed
An AI workflow that classifies each document, extracts and validates key fields, produces a summary, flags low-confidence items for review, and files and routes the rest automatically.
Implementation
We connected the document store and downstream systems, added human verification for flagged items, and kept an audit trail for every document.
Technologies used
Document classification and field extraction with validation rules, integrated with the team's storage and downstream systems.
Results
Manual data entry dropped, documents moved faster, and exceptions were caught and verified rather than missed. Outcomes are described qualitatively.
What was supported after launch
We monitored extraction quality, refined rules as new document types appeared, and supported the workflow over time.
Related services
Could this work for your process?
Describe your process and where it slows down. We turn it into structured requirements, an AI workflow and a working, supported solution.