Fintech Automation

Automating 90% of Financial Reporting for a Venture Capital Firm

How we built a secure, local-LLM agent to process P&L statements, detect anomalies, and draft investment memos in seconds.

45h+

Hours Saved/Week

99.8%

Accuracy Rate

<30s

Processing Time

12x

ROI (Year 1)

The Challenge: Data Privacy vs. Speed

Client 'Alpha Ventures' deals with sensitive portfolio data. They needed AI analysis but refused to send data to OpenAI/Claude due to privacy risks. Their analysts were spending 15 hours per week manually copying data from PDFs to Excel.

Pain Points:

❌ Risk of data leaks via public APIs.
❌ Manual entry errors costing $10k+ in bad decisions.
❌ Analyst burnout from repetitive tasks.

The Solution: Local-First RAG Pipeline

We architected a private, "air-gapped" solution using a fine-tuned Llama 3 model running on their bespoke hardware, connected to a local vector database.

1. Secure Ingestion

Built a custom Python parser that OCRs varied PDF formats (Balance Sheets, P&Ls) without any cloud dependencies.

2. Local Reasoning

Deployed Llama-3-70b quantized on 2x A6000 GPUs to perform reasoning locally.

3. Anomaly Guardrails

Programmatic code layer that validates AI math against raw Excel formulas. Zero hallucination tolerance.

Watch the Demo (58s)

Architecture Stack

PythonLangChainQdrantDockerLlama 3React

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