Introduction

A private family investment firm (confidential client), managed reporting across multiple holdings, pulling data from documents, statements, and spreadsheets by hand. Preparing
recurring reports was slow, repetitive, and error-prone. Monolythic Tech built an AI automation that ingests documents, extracts the right data, and
assembles recurring reports, freeing the team from manual back-office work.

Project Goals

• Automate intake of documents and statements from multiple sources

• Extract key figures reliably into a structured format

• Generate recurring reports on a predictable schedule

• Reduce manual effort and the errors that come with it

• Keep a person in control of review and sign-off

  
  

Challenges

1. Varied documents: files arrived in many formats and layouts

2. Accuracy: financial figures must be extracted correctly, every time

3. Repeatability: reports had to follow a consistent structure and cadence

4. Oversight: the firm needed review and approval before anything was final

5. Confidentiality: sensitive financial data had to be handled securely

   
   

Process

• Research & Discovery: documented the reporting cycle, data sources, and where time was lost.

• Workflow Mapping: mapped the path from raw documents to finished report, with checkpoints and approvals.

• Agent & Retrieval Design: designed extraction and retrieval steps to pull the right figures from the right places.

• Build & Integration: built the pipeline in n8n with LLM-based extraction, connected to the firm's tools and templates.

• Evaluation & Hardening: used Langfuse to evaluate extraction quality and trace exceptions, adding human review before issue.

  
  

Key Features

• Automated document and statement intake

• LLM-based data extraction into a structured format

• Scheduled, consistent report generation

• Human review and sign-off built into the flow

• Exception flagging for anything unusual

• Secure handling of confidential data

Outcomes

• Reports that took hours of manual work now assemble automatically

• Fewer manual-entry errors thanks to structured extraction and review

• The team spends less time on back-office tasks and more on analysis

• Every report still passes through human review before it goes out

Conclusion

By automating the repetitive parts of reporting while keeping people in control of review, the firm cut back-office effort without giving up oversight.
The project shows how AI can take on document-heavy work reliably when it's paired with extraction checks, evaluation, and human sign-off.