Case Study

Underwriting Automation

Archos AI replaced a manual underwriting workflow with AI-driven document processing and decision automation.

System PathUnderwriting automation
Archos AIBuild Layer
1Document intake
2AI extraction
3Exception review
4Decision
Outcome

The system reduced processing time, improved accuracy, and created measurable annual savings by turning a document-heavy review process into a structured automation workflow with human oversight.

The Problem

The underwriting workflow depended on manual document intake, repeated data entry, cross-system review, and status tracking. Each application required analysts to gather information from multiple sources before a decision could move forward.

That created delays, inconsistent visibility, and unnecessary operational effort for a process that needed speed, accuracy, and clear review trails.

Before

Manual review loop

  • Analysts gather documents from multiple sources by hand
  • Data re-entered across systems for each application
  • Review status tracked in inboxes and spreadsheets
  • Decisions wait on manual cross-system checks
After

Structured automation

  • Documents classified and routed automatically at intake
  • Structured data extracted once, shared everywhere
  • Exception queues surface only the cases needing judgment
  • Status visible to operations and leadership in real time

The System Archos AI Built

Archos AI designed an underwriting automation system that combined document processing, structured data extraction, rule-based routing, AI-assisted review, and human approval checkpoints.

  • Document intake and classification
  • Data extraction from underwriting materials
  • Decision support for defined review steps
  • Exception queues for cases requiring human judgment
  • Status visibility for operations and leadership teams
  • Integration planning for existing business systems

Why It Worked

The project focused on a specific operational workflow rather than a broad AI experiment. The system was designed around the documents, decision points, handoffs, and human review steps already used by the business.

By keeping humans involved where judgment mattered and automating the repetitive work around them, the workflow became faster and more consistent without removing accountability from the process.

Have a manual workflow worth automating?

Tell us what your team is reviewing, routing, or entering by hand. We will help identify the clearest path to a working automation system.

Start Your AI Build Consultation