Document parsing & extraction
Document parsing and data extraction, built as production infrastructure
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.
- 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.
Related
Custom OCR engineering
Custom OCR development for documents that off-the-shelf OCR gets wrong
Custom OCR pipeline development for scanned, photographed, and low-quality documents — layout-aware extraction, confidence scoring, vision-model escalation, and ground-truth accuracy benchmarks.
RAG systems
RAG systems that retrieve the right thing — and prove it
Design, build, and evaluate retrieval-augmented generation systems: chunking and indexing strategy, hybrid and reranked retrieval, grounded answer generation, and retrieval evaluation harnesses.
Advisory
Document AI consulting: architecture, build-versus-buy, and accuracy strategy
Independent consulting on document AI architecture, model and vendor selection, accuracy measurement, cost modelling, and build-versus-buy decisions — from an engineer who ships extraction systems.
White label
White-label bank statement parsing for your product
Put a production bank statement parser inside your own product under your own brand. White-label API, your domain, your pricing, volume-based licensing — no extraction team required.
This page is also available as markdown for AI agents: /services/document-data-extraction.md · index at /llms.txt. Canonical URL: https://parsemystatement.com/services/document-data-extraction.