Case study·CRE advisory

Document review queue

From a shared review folder — to a routed queue with named owners and an audit trail

100%
Low-confidence fields routed to a named owner with auditUp from a shared SharePoint folder
“Review stopped being a black box.”
Operations Lead · CRE advisory transformation team
UK CRE advisory firm (transformation team)·11-person review pilot covering ~120 documents per week across 3 service lines·5-week pilot·United Kingdom
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01Pilot envelope
Pilot length5 weeks
First signal7 days
First ROI30 days
Team alongside5 seats · 3 colleagues

5-week operations pilot; defined review routing by field type, confidence and risk; the queue launched in dry-run before production review.

02What it owns
Reports toOperations Lead, dotted line to data owner on extraction rule changes.
Owns
  • Review queue — every extracted field routed to a named reviewer by type, confidence and risk
  • Evidence panel — each value paired with the source paragraph and a working link to the document
  • Correction log — reviewer fixes captured with a reason code and surfaced to the data owner
  • Salesforce dry-run — proposed writes shown for approval, never pushed to production until signed off
  • Daily review board — queue load, throughput and ageing surfaced to the Operations Lead in Teams
Does not do
  • Production Salesforce writes — performed only after a named reviewer approves the dry-run
  • New extraction rules — proposes corrections; only the data owner promotes them into production
  • Confidence thresholds — surfaces the current bands, does not change them without governance sign-off
Done looks like

Reviewers open a named queue, see the evidence next to each value, and approve or correct in place. Corrections, audit and Salesforce sync are wired in by default.

03The team
AI teammates3
HannahRoutes each extracted field to a named reviewer based on field type, model confidence and risk band.
MarcusShows the source paragraph for each extracted value next to the reviewer's queue, with a permalink back to the document in SharePoint or OneDrive.
SofiaCaptures every reviewer correction with a reason code, logs it to the audit trail and proposes the change back to the extraction rules for the data owner to promote.
Human team5
  • Operations LeadOperations
  • 6 Document reviewersReview
  • 2 Data analystsAnalytics
  • Salesforce administratorSystems
  • Data ownerGovernance
04Connected stack
SharePointOneDriveMicrosoft TeamsExcelSalesforceUnify console
05What it returned
100%Low-confidence fields routed to a named owner with auditUp from a shared SharePoint folder
33%Reviewer idle time reductionAgainst the baseline shift sample
5Extraction rules updated from reviewer correctionsReused across the three pilot service lines
  • Day 0
    Co-ordinator sessionOperations Lead, reviewers, analysts and the data owner agree field types, confidence bands and the risk routing rules.
  • Day 7
    First routed batchHannah routes the first batch of extracted fields to named reviewers; Marcus shows source paragraphs alongside each value.
  • Day 14
    Dry-run liveSalesforce writeback runs in dry-run; proposed updates are shown to reviewers, nothing is written to production yet.
  • Day 21
    Approval-gated writesProduction Salesforce writes go live behind reviewer approval; Sofia logs every correction with a reason code.
  • Day 27
    Rules promotedData owner promotes the first five rule changes proposed from corrections; reviewer idle time is re-measured against the baseline.
  • Day 30
    ROI reviewSponsor signs off on three baseline metrics and approves expansion to a fourth service line.
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