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I will build a claude powered PDF and document extractor
Surya M
Über diesen Service
Note: Please message me BEFORE placing an order. Let's confirm scope on a 15-min chat so the quote is accurate.
I replace manual PDF data-entry with a Claude-powered extractor that handles messy layouts and validates output reliably.
At my current role (Senior Data Analyst, 60,000+ exam candidates) I built a production result engine: raw Excel in, validated data out, district-segmented PDF sheets for thousands of students per cycle. This gig adapts that tech to your docs.
What I deliver:
- Prompt-engineered Claude extractor with deterministic JSON
- Schema validation (Pydantic) + retry on partial extractions
- Audit logging on every extraction
- FastAPI endpoint + Railway/Vercel deploy (Premium)
- Human review queue for low-confidence results (Premium)
Tiers:
- Basic ($250): single doc type (invoices), 100-page test
- Standard ($500): multi-doc, structured JSON, retry, errors
- Premium ($1,200): full pipeline, FastAPI, review queue, deployed
Tools: Python, Claude API, FastAPI, Pydantic, PostgreSQL, PyMuPDF.
Perfect for: finance (invoices), HR (resumes), legal (contracts), EdTech (results).
Message me first so we can scope it properly.
Lerne Surya M kennen
Surya M
Data and AI Automation Consultant, Python Claude ETL
- AusIndien
- Mitglied seitJuni 2025
- ⌀ Antwortzeit1 Stunde
Sprachen
Telugu, Englisch, Hindi
Data and AI Automation Consultant. 4+ years building production data systems for EdTech, 85,000+ students served across online bootcamps and offline coaching institutes.
I ship ETL pipelines (Python, PostgreSQL) unifying Zoho CRM, Google Sheets, and LMS platforms into a single source of truth, plus reporting autopilots and autonomous Claude-powered AI agents that eliminate 20+ hours per week of manual work.
Best fit: EdTech, coaching, SMBs with scattered data. Tech: Python, SQL, FastAPI, Claude API, Zoho CRM, Google Sheets API, WATI.
Mein Portfolio
FAQ
What is my cost for Claude API usage?
Typical extraction runs $0.003 to $0.03 per page depending on model (Sonnet vs Opus). I will share a token estimate upfront so there are no surprises. You control the Anthropic account and pay Anthropic directly.
How accurate is the extraction?
On structured docs (invoices, forms) I target at least 98 percent field-level accuracy, measured on your test set. On unstructured docs (contracts, resumes) it depends on the schema, and I tell you upfront if a field is risky.
Can the pipeline handle scanned PDFs (images)?
Yes. I use OCR pre-processing (Tesseract or Claude vision support for scans) before the extractor. Scanned docs cost slightly more tokens but accuracy is comparable.
