Data Extraction & Intelligent Automation

// the problemThe Problem

The most expensive copy-paste in your company

Every company has at least one workflow that looks like this: information arrives in an unstructured format (an email, a scanned document, a PDF attachment), and a person manually reads it, interprets it, and types the relevant fields into another system. It's tedious, error-prone, and it doesn't scale.

Traditional automation (regex, templates, rule-based parsers) breaks the moment the format changes. LLM-powered extraction is different — it understands the content, adapts to variations in layout and language, and can be validated against schemas so you catch errors before they propagate.

// what these look likeWhat These Look Like

The spectrum

Extraction and automation projects range from simple document parsing to fully orchestrated workflows that ingest, extract, validate, route, and act on data without human intervention — unless something needs review.

◆ Document extraction straightforward

Pull structured fields from a known document type — invoices, contracts, reports, forms. Define the schema you need, point the pipeline at the documents, and get clean data out the other end. Handles layout variations, different formats, and messy scans without rigid templates.

◆ Multi-source ingestion moderate

Data arrives from multiple channels — email attachments, uploaded files, API webhooks, web forms — in different formats. The pipeline classifies what it's looking at, applies the right extraction strategy for each source, normalizes the output, and loads it into your system of record. One pipeline, many inputs.

◆ Intelligent workflow automation advanced

Extraction is just the first step — the system also classifies, routes, and acts on the data. An incoming email gets parsed, the extracted data triggers downstream actions (creating records, notifying people, updating dashboards), and edge cases get flagged for human review instead of silently failing. End-to-end automation with humans in the loop where it matters.

// examples of what I've builtExamples

This looks like

Email-to-database pipeline

Operations team receiving hundreds of emails daily containing structured data buried in free-text bodies and attachments. Built an ingestion pipeline that parses incoming emails, extracts key fields using LLM-powered parsing, validates against known schemas, and pushes clean records into the system of record — replacing hours of manual data entry per day.

Invoice processing automation

Finance team manually keying invoice data from PDFs — vendor, amounts, line items, dates, PO numbers — across dozens of different vendor formats. Built an extraction pipeline that handles layout variations, validates extracted amounts against PO data, flags discrepancies for review, and pushes clean records to their accounting system.

Call recording QA pipeline

Quality assurance team manually reviewing recorded calls against a compliance checklist — a full-time job for multiple people. Built a pipeline that transcribes recordings, evaluates each call against the checklist using LLM analysis, and produces scored reports with evidence citations. Turned a manual review process into a review-and-approve workflow.

Contract metadata extraction

Legal team needed structured metadata from thousands of existing contracts — parties, effective dates, renewal terms, key clauses, obligations. Built an extraction system that processes contracts in bulk, populates a searchable database, and flags contracts approaching renewal or containing specific clause types.

Multi-format report consolidation

A team receiving weekly reports from dozens of partners — each in a different format (PDF, Excel, CSV, email body). Built a pipeline that ingests all formats, normalizes the data into a common schema, reconciles duplicates and inconsistencies, and produces a unified dataset that feeds their reporting dashboard.

// under the hoodHow It Works

How these are built

These aren't scripts held together with regex. They're production pipelines designed to handle messy real-world data reliably — with validation, error handling, and monitoring built in from the start.

Document parsing

Different documents need different strategies. PDFs with selectable text get text extraction; scanned documents get OCR. Tables, headers, and multi-column layouts are handled with layout-aware parsing rather than treating the page as a flat string. The goal is clean text input before any LLM touches it.

LLM-powered extraction

Structured output generation with the right model for the job. Schema-constrained extraction so the LLM returns exactly the fields you need in the format you need. Smaller, faster models for high-volume extraction; more capable models for complex or ambiguous documents. Prompt design that handles edge cases and variation without breaking.

Validation & confidence scoring

Extraction without validation is just faster mistakes. Every pipeline includes schema validation (are the fields the right type and format?), cross-reference checks (does the extracted PO number exist in your system?), and confidence scoring so low-confidence extractions get flagged for human review instead of silently entering your database.

Routing & integration

Clean data needs to go somewhere. I build integrations into your existing systems — databases, CRMs, ERPs, accounting software, Slack notifications, webhooks — so extracted data flows into your workflows automatically. Classification logic routes different document types to different destinations.

Monitoring & error handling

Production pipelines need to handle failures gracefully — malformed documents, unexpected formats, API timeouts, extraction confidence below threshold. I build in retry logic, dead-letter queues for failed documents, alerting for anomalies, and dashboards so you can see pipeline health at a glance.

// how we work togetherWorking Together

The process

Scoping call

30 minutes, free. You walk me through the workflow — where the data comes from, what format it's in, where it needs to go, and what happens when things go wrong today. I'll tell you honestly how much of it can be automated and where human review should stay in the loop.

Sample analysis & technical spec

I review a sample of your actual documents — the clean ones and the messy ones. Then I write a technical spec covering the extraction approach, validation rules, integration points, and expected accuracy. We agree on scope, success criteria, and what "good enough" means for your use case.

Build & validate

I build the pipeline in stages — parsing first, then extraction, then validation, then integration — measuring accuracy at each step against your sample data. You see working output early and flag edge cases I need to handle. The pipeline gets tuned against real-world variation, not just clean test cases.

Deploy & hand off

The pipeline goes into production with monitoring, alerting, and documentation. I include a runbook covering common failure modes and how to handle them, so your team isn't dependent on me for day-to-day operations.

// pricingPricing

What this costs

It depends on the complexity of your documents, the number of source formats, and how much downstream integration is involved. A single-format extraction pipeline is a smaller engagement than a multi-source ingestion system with classification, routing, and full workflow automation.

The scoping call is free and there's no obligation. I'll review your documents, give you an honest assessment of what can be automated, and tell you whether the ROI makes sense for your volume.

// common questionsCommon Questions

Frequently asked

Q: How accurate is the extraction?

It depends on the document type and field complexity, but well-built pipelines typically achieve 90–98% accuracy on structured fields. The key is that every pipeline includes confidence scoring and validation — low-confidence extractions get flagged for human review rather than entering your system unchecked. You set the accuracy threshold based on what's acceptable for your use case.

Q: What happens when the format changes?

This is where LLM-powered extraction beats traditional approaches. Regex and template-based parsers break when layouts shift; LLM extraction adapts to variations because it understands the content, not just the position. That said, I also build in format detection and alerting so you know when a new format appears that needs attention.

Q: Can this handle scanned documents and handwriting?

Scanned documents, yes — OCR is a standard preprocessing step and modern OCR handles most print quality well. Handwriting is harder and depends on legibility; I'll be upfront about expected accuracy after reviewing your samples. For critical fields in poor-quality scans, I design the pipeline to flag for human review rather than guess.

Q: What about data privacy?

Your documents stay in your infrastructure. I can build on self-hosted models for extraction if needed, use zero-retention API agreements, or set up data anonymization for sensitive fields before they reach any external service. I've built in regulated environments and can sign NDAs and BAAs.

Q: How do we know the ROI is worth it?

I'll help you do the math during the scoping call. It usually comes down to: how many hours per week is someone spending on this manually, what's the error rate, and what does a mistake cost? For most teams processing more than a few dozen documents per day, the payback period is measured in weeks, not months.

Q: Can our team maintain this after handoff?

Yes. Everything is built with standard tools, version controlled, and documented. I include a runbook for common operations — adding new document types, adjusting validation rules, handling failed extractions. Ongoing support is available if you want it, but the goal is independence.

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