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PP AdvisorsProfessional Services

Advisory Workflow & Document Engine

Cut the grind of inbound PDFs and email packets into structured client work—review queues, not inbox chaos.

Problem statement

What was broken in the real operation

PP Advisors received client packets as email attachments and scanned PDFs—bank statements, forms, contracts. Analysts manually renamed files, copied figures into worksheets, and tracked “who owns this client this week” in their heads. Turnaround slipped when volume spiked, and junior staff repeated the same extraction mistakes.

A professional advisory practice where expertise is the product, but throughput was gated by document handling. The firm didn’t need another generic CRM—they needed the documents to become structured work items with confidence scores and a review path.

Operational challenges

  • Inbound documents arrived in inconsistent formats (scan vs. digital PDF)
  • No reliable extraction → analysts retyped numbers into sheets
  • Review ownership was informal; items fell between people
  • Clients asked “did you get my file?” with no receipt trail

What Revilen did

Solution approach

We designed an inbound document engine: store the raw file, OCR when needed, extract structured fields with LLM guardrails, then open a human review queue for medium/low confidence. Accepted packets become advisory workflow tasks—not buried email.

What we built

  • Secure document ingest with original file retention
  • OCR + LLM extraction into typed schemas
  • Analyst review UI with field-level confidence
  • Client packet status the team can answer from one place

Systems delivered

Document ingestExtraction pipelineReview queueAdvisory tasking

Stack

PythonLLMsOCRReactREST APIsPostgreSQL

Outcomes

What changed for PP Advisors

  • Analysts spent time on advice, not copy-paste
  • Duplicate or incomplete packets were caught before work started
  • Review backlog became visible and assignable
  • Inbound volume stopped creating silent quality debt

Facing a similar operational mess? We’ll map the real problem—then build the system.