You will get Extract structured data from PDFs and documents with an LLM pipeline
Rising Talent
Project details
Send me your documents — invoices, contracts, statements, forms — and get back clean structured data you can use, with every value traceable to where it came from.
The problem with LLM extraction is that it will confidently invent a value that isn't there, and a hallucinated total looks exactly like a real one — so you never catch it. This pipeline is built around that. Every field I extract carries a pointer to the exact line it was read from; if a value can't be traced to the document, it doesn't ship. Where a document doesn't state a figure, it's recorded as absent — never computed from the surrounding numbers and passed off as real.
Each field also carries a confidence score. Anything below threshold, or read from unclear source text, goes to a review queue with a plain reason and the source shown — so you confirm it rather than re-key it. Nothing is guessed at, nothing silently dropped.
You get the structured data in your format (CSV, JSON, or direct database insert), a report showing every value with its source and confidence, and the flagged fields listed for your decision. I handle native PDFs, scanned documents with OCR, images, Word, and email.
The problem with LLM extraction is that it will confidently invent a value that isn't there, and a hallucinated total looks exactly like a real one — so you never catch it. This pipeline is built around that. Every field I extract carries a pointer to the exact line it was read from; if a value can't be traced to the document, it doesn't ship. Where a document doesn't state a figure, it's recorded as absent — never computed from the surrounding numbers and passed off as real.
Each field also carries a confidence score. Anything below threshold, or read from unclear source text, goes to a review queue with a plain reason and the source shown — so you confirm it rather than re-key it. Nothing is guessed at, nothing silently dropped.
You get the structured data in your format (CSV, JSON, or direct database insert), a report showing every value with its source and confidence, and the flagged fields listed for your decision. I handle native PDFs, scanned documents with OCR, images, Word, and email.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$10
|
Standard
$20
|
Advanced
$30
|
|---|---|---|---|
| Delivery Time | 1 day | 4 days | 8 days |
Number of Pages Mined/Scraped | 50 | 500 | 5000 |
Number of Sources Mined/Scraped | 5 | 15 | 30 |
Number of Revisions | 1 | 2 | 3 |
Optional add-ons
You can add these on the next page.
Rush delivery (24 hours)
+$5
Human-in-the-loop review interface
(+ 2 Days)
+$5
Deploy to your cloud
(+ 2 Days)
+$10Frequently asked questions
About Bhavin
Data science, Automation, ETL and Machine learning
Surat, India - 9:30 pm local time
For the past year I worked as a quantitative researcher at a global trading
firm, where I built an autonomous pipeline that runs hypothesis → validated
result → report in a single unattended, multi-day run. That taught me the part
most AI automation skips: an agent that fails silently is worse than no agent.
So I engineer the guardrails — gating, retries, audit trails, human sign-off on
anything irreversible — as carefully as the capabilities.
WHAT I BUILD
Data engineering & ETL — ingestion from APIs, databases, files and scraped
sources; schema design, incremental loads, deduplication and entity resolution.
Python · DuckDB · Postgres · Parquet · pandas. Pipelines that are idempotent,
monitored, and safe to re-run.
AI agents & LLM systems — agentic workflows over your own APIs and tools;
extraction from documents and unstructured text with schema enforcement and
citation-grounded output; RAG; batch processing at scale; and fixes for
demo-grade agents that break in production. Claude Code · MCP · Agent SDK · REST.
Machine learning — feature engineering, model selection, time-aware and
walk-forward validation, calibration, honest error analysis.
scikit-learn · LightGBM · statsmodels.
Analytics & reporting — exploratory analysis, hypothesis testing, dashboards,
and a write-up that says what the data actually supports.
SCALE
I run an independent research program on reconstructed L3 order-book data —
2.3B+ messages — so large, messy, high-volume datasets are normal for me rather
than exceptional.
HOW I WORK
Discipline you can inspect: a registry of dead ends, drafts I retract when the
data was wrong, replication failures reported next to successes. An audit trail
you can trust, not just a result.
Tell me what you're trying to automate or figure out, and I'll tell you honestly
whether the approach is right — including when it isn't.
Steps for completing your project
After purchasing the project, send requirements so Bhavin can start the project.
Delivery time starts when Bhavin receives requirements from you.
Bhavin works on your project following the steps below.
Revisions may occur after the delivery date.
Ingest your documents
I read each document as text and lock the line numbers, so every value I later extract can be traced back to the exact place it came from.
Extract fields with confidence scoring
An LLM pipeline extracts each field, with a confidence score per value and a pointer to the source line. A field the document doesn't contain is marked absent, never invented.