Hi everyone! 👋
I'm a Python developer with 12+ years of experience in backend engineering. Recently, I started building my own SaaS product called StatementIQ to simplify bank statement analysis using AI.
When I started working on this, I quickly realized that analyzing bank statements in production is much harder than just extracting raw text from a PDF.
The Real-World Engineering Challenges
Building a reliable parser means handling messy edge cases:
- Inconsistent Formats: Every bank has a completely different layout, multi-line descriptions, and column structure.
- Locked & Scanned Files: Handling password-protected PDFs alongside OCR fallbacks for scanned copies.
- Transaction Categorization & Insights: Accurately identifying salaries, EMIs, recurring expenses, and cashflow patterns without hallucinating numbers.
- Scale & Determinism: Processing large multi-page financial statements quickly while keeping the output strictly structured (JSON) and reliable.
Under the Hood (Tech Stack & Learnings)
My goal is to build a production-grade tool, not just another wrapper or demo project. Through this journey, I'm diving deep into:
- Backend & Data Processing: Python (FastAPI/Django), PostgreSQL, and asynchronous task queues for handling heavy files.
- AI & LLMs: Combining deterministic rule-based parsing with LLMs (including self-hosted models via Ollama) for fallback column mapping and categorization.
I'm actively improving the product and shipping new features step by step. You can check out the live project here: 👉 StatementIQ
I’d love to connect with fellow Python developers, AI engineers, and anyone building in the FinTech space!
Question for the community:
When building real-world AI applications on messy documents, what has been your biggest bottleneck—data quality, accuracy, or latency/performance?
Thanks for reading!
— Nitesh Kumar Singh (Portfolio)
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