Case Study • Professional Services
Document Processing Automation
A mid-sized firm replaced a manual intake and extraction workflow with a production AI pipeline, improving speed and consistency while keeping human review where it mattered.
By Phil Maher • Published
Cycle Time
-75%
Manual Hours
30+/week saved
Extraction Errors
8% to <2%
Business Context
The client handled 200+ inbound documents each week across contracts, financial records, and client onboarding packets. The team relied on manual triage and spreadsheet-based extraction. As volume increased, SLA misses and rework increased with it.
What Was Broken
- No standardized intake path across channels (email, portal uploads, ad hoc shares).
- High error rates from manual field extraction and inconsistent routing rules.
- Senior staff spent time on repetitive classification instead of client-facing work.
Implementation Approach
I designed a staged pipeline: OCR normalization, document classification, schema-based extraction, confidence scoring, then rules-driven routing into existing systems. Low-confidence items were automatically queued for human review with side-by-side source previews.
Architecture Highlights
- Deterministic preprocessing before model calls to reduce token cost and variance.
- Validation layer enforcing required fields and business constraints.
- Audit trail for every extraction decision and reviewer override.
- Progressive rollout by document type to de-risk adoption.
Delivery Timeline
- Week 1: workflow discovery, schema mapping, baseline metric capture.
- Weeks 2–3: extraction prototype and confidence/routing logic.
- Week 4: user acceptance testing with operations leads.
- Weeks 5–6: production rollout, monitoring, team handoff.
Outcome
Within the first month post-launch, average processing time dropped by 75%. The team recovered over 30 hours per week, while extraction quality improved enough to move reviewers from first-pass data entry to exception handling only. The client used the same architecture pattern to expand automation into two adjacent workflows.
Need This in Your Workflow?
If your team is still moving data manually between documents and systems, this is usually one of the fastest paths to measurable ROI.