# Document parsing and data extraction, built as production infrastructure

> Turn unstructured PDFs, scans, and emails into validated structured data. Custom document parsing pipelines with schema design, confidence scoring, human review queues, and measurable accuracy.

Source: https://parsemystatement.com/services/document-data-extraction
Updated: 2026-09-17

Most document-extraction projects fail at the boundaries, not the model: no agreed schema, no validation, no confidence threshold, no plan for the 5% of documents that will always need a human. I design and build the whole path — ingestion, extraction, validation, review queue, and the API your product actually calls — with accuracy measured against ground truth at every step.

**At a glance**

- Schema design first, so downstream systems get a stable contract
- Confidence thresholds and human-review queues for the long tail
- Idempotent, retryable pipelines that survive provider outages
- Delivered as an API your product can call, not a notebook

## The part nobody scopes

Extraction demos are easy. You pick three clean documents, prompt a capable model, and the output looks perfect. The project then stalls for six months on everything that demo skipped.

What it skipped: what the output schema is and who owns it when it changes. What happens when the model returns nine fields instead of ten. How a document that scored 0.4 confidence gets in front of a human without blocking the queue. What the per-document cost is at 100,000 documents a month and whether that is still viable. How you know next month's model upgrade did not silently make things worse.

I build for those questions from day one, because they are what determines whether an extraction system reaches production or quietly gets shelved.

## What the build includes

### Schema and contract design

A typed output schema, versioned, with explicit nullability and units. Downstream teams get a stable contract and a documented migration path when fields change.

### Ingestion and routing

Email, SFTP, S3, webhook, or direct upload. Deduplication, virus scanning, page splitting for multi-document files, and classification so each document reaches the right extractor.

### Extraction and normalization

The right method per document family — native text, OCR, vision model, or a layout model — followed by a normalization step that produces the agreed schema with per-field confidence.

### Validation and reconciliation

Domain rules that catch what confidence scores miss: totals that must sum, dates that must fall in range, cross-field consistency, running-balance checks, and duplicate detection across documents.

### Human-in-the-loop review

A review queue fed by confidence thresholds and failed validations, with side-by-side document and field editing. Corrections are captured as new ground truth, so the benchmark improves as the system runs.

### Delivery API

A documented HTTP API — async upload, status polling, result fetch — plus optional MCP server so AI agents can call the same pipeline. Keys, quotas, rate limits, and per-key usage logs included.

## The path to production

### 1. Schema workshop

We define exactly what fields you need, their types, and which are strictly required. This is the single highest-leverage hour in the project and it happens before any code.

### 2. Ground truth and baseline

A hand-written ground-truth set from your real documents, and a first scored run. Now every later decision is measurable.

### 3. Pipeline build

Routing, extraction, normalization, validation, storage. Built iteratively against the benchmark, in your repository, visible from week one.

### 4. Review queue and thresholds

We tune the confidence threshold against your actual tolerance: how many documents you are willing to review by hand versus how many errors you are willing to let through. That trade-off is a business decision and I make it explicit rather than picking a number for you.

### 5. Load, cost, and handover

Throughput testing at your target volume, a per-document cost model, structured logging and alerting, and a runbook your on-call team can actually use.

## Where documents typically come from

| Source | Typical challenge | How it is handled |
| --- | --- | --- |
| Customer email attachments | Mixed formats, multiple documents per PDF | Classification and page-level splitting before extraction |
| Bank and card portals | Column layouts that differ per institution | Layout-aware extraction with per-bank profiles |
| Supplier invoices | Variable line-item tables, multi-page continuations | Table-structure extraction with total reconciliation |
| Phone photos from field staff | Skew, glare, partial captures | Image preprocessing, vision escalation, review queue |
| Legacy scanned archives | Degraded quality, inconsistent scanning | Batch reprocessing with per-page confidence triage |
| Third-party APIs | Schema drift without notice | Contract validation with alerting on unexpected shapes |

## Engagement options

Indicative ranges; scope is fixed in writing after the discovery call.

### Discovery & architecture — from $1,400 (1–2 weeks)

Schema design, document analysis, and a written architecture plus cost model you can build against — with or without me.

- Output schema definition
- Document corpus analysis
- Reference architecture and provider comparison
- Per-document cost and latency model
- Delivery plan with milestones

### Production pipeline — from $3,500 (4–12 weeks)

End-to-end extraction infrastructure delivered into your stack and running on your volume.

- Ingestion, extraction, validation, storage
- Confidence-driven human review queue
- Documented API and optional MCP server
- Evaluation harness and CI accuracy gate
- Load testing, runbook, and handover

### Managed extraction — from $2,200 / month (Rolling, 3-month minimum)

I run and improve the pipeline so your team does not have to hire for it.

- New document families onboarded continuously
- Accuracy and cost monitoring with monthly reporting
- Provider and model migration handled
- Priority incident response

## Frequently asked questions

### We already use an IDP vendor. Is this a replacement?

Not necessarily. Sometimes the right answer is to keep the vendor and build the validation, routing, and review layers around it — that is a cheaper project with most of the accuracy gain. The audit tells us which. I have no incentive to recommend a rebuild you do not need.

### What volume does this handle?

The architecture is queue-based and horizontally scalable; throughput is a function of worker count and provider rate limits rather than a fixed ceiling. Load testing at your target volume is part of the build, and the cost model is produced before you commit to it.

### Can you work with our existing engineering team?

That is the preferred mode. I build in your repository, against your conventions, with your team reviewing pull requests. The goal is that your engineers can extend the pipeline after handover, which does not happen if I build it in isolation.

### What about documents in languages other than English?

Multilingual extraction is supported by the OCR and vision models used, but accuracy varies significantly by language and script. If non-English documents are a meaningful share of your corpus, they go into the ground-truth set so the number is measured rather than assumed.

### How do you handle sensitive documents during the engagement?

Under a signed NDA and DPA, with the option to work entirely inside your environment so no document ever leaves your infrastructure. For highly sensitive corpora I work from redacted or synthetic samples for development and validate against real data only inside your network.

## Start with the schema, not the model

Tell me what fields you need and what your documents look like. I will come back with an architecture, a cost model, and an honest view on whether this is a four-week project or a four-month one.

Contact: adarsh@parsemystatement.com · https://parsemystatement.com/contact

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