Muhammad Adrees
AI Engineer | LLM Workflows & Agents for B2B SaaS
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Problem💯
When a small business applies for a loan or cash advance, an underwriter has to answer one question: can this business pay us back? To answer it, they manually review months of bank statements, tax returns, and business documents, calculating cash flow, spotting risks, scoring against the lender's policy, and writing a recommendation.
This takes a skilled human 4-8 hours per file, and lenders process hundreds per week. It's slow, expensive, and two underwriters often reach different conclusions on the same file.
What we built🙌
- A platform that compresses this work from hours into minutes, while keeping a human in control of the final decision.
- The system ingests applications and supporting documents, extracts and validates the underlying financial data, evaluates it against the lender's risk policy, and produces a reviewable recommendation the underwriter can approve, modify, or reject.
- AI handles the heavy lifting, reading documents, summarizing patterns, generating draft assessments.
Outcomes🎉
- Underwriting review time reduced from ~6 hours to under 30 minutes
- 5-10× more files processed per underwriter
- Eliminated manual data entry from bank statements
- Consistent, explainable decisions with full audit trails
Stack🧰
FastAPI, Python, PostgreSQL, Redis, GCP, React, TypeScript, and a mix of LLM and document-AI tooling.
Best fit for🔥
Lenders, fintech startups, credit and risk teams, MCA/lending platforms, and anyone building AI for financial workflows where decisions need to be fast, auditable, and trustworthy.
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AI Document Processing — Turning Messy Financial Documents Into Clean Data
The problem💯
Every financial workflow starts with a stack of messy documents,
bank statements in inconsistent formats, scanned PDFs of varying
quality, multi-page reports with tables that span columns, and data
that comes out of OCR looking nothing like the original.
Teams spend hours manually re-typing transactions, fixing extraction errors, and double-checking numbers before they can do anything useful with the data. It's the slowest, most error-prone step in every back-office workflow.
What I built🙌
- A cloud-native document processing platform that turns these messy
inputs into structured, validated, trustworthy data.
- Users upload financial documents, bank statements, invoices, scanned
reports and the system handles the rest: parsing, normalization,
reconciliation against statement totals, confidence scoring, and
review-ready output.
- AI does the heavy extraction work, deterministic validation catches errors before they reach the user.
Outcomes🎉
- Document processing time reduced from hours to under 5 minutes
- Eliminated manual transaction re-entry from bank statements
- Auto-reconciled statement totals catch extraction errors before data is exported
- Versioned results so users can compare AI outputs and track corrections over time
- Export-ready data that drops cleanly into downstream systems
Stack🧰
FastAPI, Python, GCP, Firestore, document AI tooling, React,
TypeScript.
Best fit for🔥
Fintech teams, accounting platforms, lenders, back-office automation
products, and anyone building workflows where document quality is
the difference between automation that works and automation that
creates more cleanup work than it saves.
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