Custom Document Processing Automation
When contracts, invoices, SOWs, and compliance docs arrive in PDF and someone re-keys the contents into your systems before anything else can happen.
Document workflows are where service operations lose hours quietly. Contracts arrive countersigned in PDF. Vendor invoices come in by email attachment. SOWs land in shared drives. Compliance documents get filed in folders. None of it is structured data, and nothing downstream can act on it until a human extracts the relevant fields and re-enters them into the right system. We build a parsing, validation, and storage pipeline that turns documents into structured data the rest of your stack can consume. The PDF still exists as the legal record; the data inside it stops being trapped there.
Pressure-test your bottleneck›Common stack today
- Email inboxes for receiving documents
- Google Drive, Dropbox, or SharePoint for storage
- DocuSign or PandaDoc for outbound contracts
- QuickBooks, Xero, or NetSuite for AP
- Generic OCR tools for first-pass extraction
- Manual data entry for the cases that matter
Where no-code tops out for document processing
The format variability problem is the first one. Vendor invoices come in dozens of layouts. Contracts come from law firms with different templates. Insurance certificates have a standard but every carrier formats it slightly differently. OCR alone is not enough; the same field appears in different positions, under different labels, with different formats, depending on the source. Templated parsing tools handle the highest-volume layouts and fail on the long tail.
The validation problem is the second one. Extracted data is only useful if you can trust it. Real validation involves checking extracted values against business rules, against other systems of record, and against expected ranges. An invoice total that doesn't match the line items is a parsing error. A contract effective date earlier than the signature date is a parsing error. A purchase order missing a vendor reference is a parsing error. Catching these requires logic, not just OCR confidence scores.
The downstream integration problem is the third one. Once the data is structured, it has to land in the right system: invoices into accounting, contracts into a contract repository with the right metadata, compliance docs into the compliance log with the right tags. Each destination has its own field map, and the field maps change over time. Templated automations handle one destination at a time and don't compose.
And the audit trail problem is the fourth one. For documents that matter (and most of these do), regulators, auditors, and legal review want to know what was extracted, what was validated, what was changed by a human, and when. No-code tools rarely produce that trail.
What we build
We build a document processing pipeline with three explicit stages: extract, validate, route. Extraction uses the right tool for each document class (modern OCR plus structured parsers, or LLM-based extraction for unstructured language) and produces structured data with confidence scores. Validation checks the data against your business rules and your existing systems. Routing places the validated data into the right destination with the right metadata.
Documents that pass extraction and validation cleanly flow through automatically. Documents that fail surface to a review queue with the source PDF and the extracted data side by side. A human reviews, corrects, and approves; the correction trains future extractions for that document class. The audit trail covers every step.
The pipeline runs in your cloud. The PDFs and the extracted data live in your storage. Confidential documents never leave your perimeter. Existing systems stay where they are; the pipeline is the layer that gets the contents of every document into them, with structure and provenance attached.
How it's built
- Python for the pipeline logic and validation
- Modern OCR plus document layout parsers
- Optional LLM-based extraction for unstructured fields
- PostgreSQL for extracted data and audit trail
- React for the human review queue
- Deployed to your AWS, Azure, or GCP account
Frequently asked
How accurate is the extraction?
Accuracy varies by document class and source quality, and we report it explicitly. For high-volume document classes from a small set of senders (your top vendor invoices, your standard contract template), we typically reach high accuracy quickly because the layouts repeat. For long-tail documents, accuracy is lower at first and improves as the system sees more examples. The pipeline is designed around this reality: high-confidence extractions flow through, low-confidence ones go to human review, and human corrections improve future extractions for that class.
Are you sending our documents to a third-party AI service?
Only if you want us to. Many engagements run extraction entirely inside your cloud account using on-premise or cloud-hosted OCR and parsing models, including AWS Textract or Google Document AI deployed in your own account. For document classes where LLM extraction adds value, we offer multiple deployment options including private model endpoints and self-hosted models. For documents covered by HIPAA, attorney-client privilege, or other confidentiality requirements, we keep everything inside your perimeter. The choice is explicit, not buried in a default.
How does the human review step work?
Documents that don't pass automated extraction or validation land in a review queue. A reviewer opens the queue, sees the source PDF and the extracted fields side by side, corrects what's wrong, and approves. Approval routes the data to the destination system. Corrections are stored as training signal for future extractions of that document class. We design the queue to be fast: typical review takes seconds for clean documents and a minute or two for messy ones, far less than re-keying from scratch.
What about regulated document workflows like HIPAA or financial compliance?
Regulated document workflows get treated differently from general document processing. Everything stays inside your cloud account; documents do not leave your security perimeter for processing. Audit trails cover every extraction, validation, correction, and routing event with timestamps and operator identity. Access to documents is logged at the field level. Encryption is at rest and in transit. We work with your security and compliance team on the specific controls your environment requires (HIPAA, SOC 2, PCI, FINRA, FERPA), and we do not use a one-size-fits-all template for regulated workflows. The integration is shaped to fit your existing audit and access-review process, not to introduce a new one.
What kinds of documents do you typically handle?
Vendor invoices, customer contracts, SOWs, master service agreements, certificates of insurance, W-9s and tax forms, regulatory submissions, lease agreements, and purchase orders are the common categories. The pattern is the same across them: the document arrives in PDF, contains specific fields the business needs as structured data, and gets routed to one or more destination systems. PandaDoc and DocuSign are common sources for outbound contracts that come back countersigned; the pipeline handles those alongside inbound documents from external vendors. The pipeline accommodates new document classes as your operation requires.
Written and built by Charles Borden, founder of AutomationsHQ. Ten years of production systems engineering before this: ship control at Electric Boat, radar positioning at Raytheon. AutomationsHQ writes custom workflow automation for service operations whose stacks have outgrown Airtable, Zapier, and Make. Real production systems, not no-code patches. Mid Bay News reclaimed 100+ hours per week of manual work after we rebuilt their content aggregation pipeline.
Industries that need this
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When matter intake, deadline tracking, and billing workflows stop scaling on Clio rules and manual document routing.
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Custom Workflow Automation for Insurance Agencies
When quote generation, policy renewals, commission reconciliation, and claims handoffs stop scaling on Applied Epic, AMS360, and manual carrier portal workflows.
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