Flagship
Financial Intelligence OS | Flagship Automation
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Financial Intelligence & Reporting OS
Custom AI ingestion + portfolio analytics layer transforming document processing and regulatory reporting.
Flagship automation case study featuring document processing and ai implementation.
14 days → 3 hours
Quarterly Reporting Time
12 min → 45 sec
Document Processing
2.5×
Scale Without Hiring
Overview
A financial services company serving high-net-worth clients with multi-entity portfolios faced mounting pressure from regulatory oversight and client expectations for real-time visibility. Their operations team received thousands of documents every month — bank statements, K-1 partnership forms, W-2 income statements, paystubs, tax returns, mortgage statements, brokerage account reports, and complex entity documents from trusts and LLCs. Each document required manual review, data entry, cross-referencing, and filing. Advisors spent their days processing paper instead of advising clients, and quarterly reporting cycles consumed the entire team for weeks at a time.
Business Context
The firm's growth was constrained by its operational capacity. Every new client added more documents to process, more data to reconcile, and more reporting to produce. The compliance team lived in constant anxiety about data accuracy — one error in a regulatory filing could trigger an audit. Client satisfaction scores were declining because advisors couldn't provide timely portfolio updates. The executive team calculated that hiring additional operations staff would erode margins to unsustainable levels. They needed a technology solution that could transform document chaos into structured intelligence while maintaining the accuracy standards required for regulatory compliance. ZapWizards was engaged to build a complete Financial Intelligence OS from the ground up.
How We Built It
We designed a multi-layered document intelligence architecture starting with automated ingestion from email, client portals, and scanned uploads. The first layer uses RAG-based document classification to identify document types with high accuracy — distinguishing between a K-1 and a 1040, or a bank statement versus a brokerage statement. The OCR layer extracts text with confidence scoring, flagging low-confidence extractions for human review rather than accepting errors. Custom extraction logic handles the specific fields needed for each document type, with validation rules that catch anomalies (for example, if extracted debt exceeds extracted assets, the document is flagged for review). The Financial Computation Engine aggregates extracted data to automatically calculate net worth, investment exposure by sector and geography, and risk metrics. We built a RAG-powered Advisor Assistant that allows advisors to query client portfolios using natural language — asking questions like 'What is John Smith's exposure to real estate?' and receiving instant answers. The quarterly reporting module automatically generates PDF reports with charts, tables, and AI-written narrative insights, reducing what was a two-week process to a three-hour review cycle.
Challenges
1
90% of documents were unstructured PDFs
2
Regulatory rules required data accuracy
3
No unified portfolio view for clients
4
Advisors manually created summaries
5
Quarterly reporting took weeks
6
No standardized onboarding processes
What We Delivered
Full document classification engine (K-1, 1040, W-2, bank statements, brokerage)
Property-level OCR with confidence scoring
Extraction logic validated via custom rules (e.g., debt > assets triggers review)
Financial Computation Engine with net worth auto-calculation
Exposure analysis by sector, geography, risk
RAG-Powered Advisor Assistant for natural language queries
Automatic narrative generation for reports
PDF quarterly report generator with automated charts, tables, insights
Tech Stack
Custom backend, RAG document classification, OCR with confidence scoring, Portfolio analytics engine, Snowflake / Google BigQuery integration
Tags
FlagshipDocument ProcessingAI ImplementationReportingWorkflow AutomationOpenAIRetoolMake.comAI Automation
Results
14 days → 3 hours
Quarterly Reporting Time
12 min → 45 sec
Document Processing
2.5×
Scale Without Hiring
Strategic Impact
The transformation from 14 days to 3 hours for quarterly reporting alone freed up hundreds of advisor hours per quarter for client-facing activities. Document processing acceleration from 12 minutes to 45 seconds per document means the operations team can handle 2.5x the client base without additional hiring. Advisors now spend their time on high-value strategic conversations rather than data entry. The accuracy improvements have eliminated compliance anxiety — every data point has a clear audit trail back to the source document. Client satisfaction improved dramatically as advisors can now answer portfolio questions in real-time during calls. The firm has been able to grow their client base significantly while actually reducing operational stress, creating a sustainable growth model that competitors cannot match with manual processes.
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