02: Document & Data Processing
Document & Data Processing
Intake, extraction and structuring of contracts, invoices and records, at the speed a growing business demands.
Overview
Contracts, invoices, and records accumulate faster than any team can read them, arriving as PDFs, scans, emails, and spreadsheets, each demanding the same manual sequence of opening, reading, and re-entering into whatever system is supposed to hold it. Signalward builds the intake layer that does this instead: documents read on arrival, fields extracted and validated, records routed to the systems that depend on them, without a person retyping a line. Built for operators and growing businesses moving at a pace paperwork was never designed to match. This is for the team that has hired someone whose actual job is opening PDFs: an insurance agency comparing carrier proposals line by line, an accounts payable clerk keying invoice totals into an ERP, a records team retyping intake forms that arrived as scans. The failure mode is always the same shape: the work is genuinely repetitive, but it demands enough judgment (does this line item match the purchase order, does this clause conflict with that one) that nobody trusted a script to do it until the script could show its work. It stops at the point where a value can't be verified against the source. Signalward's extraction pipelines are built to hold anything uncertain for a person to check rather than guess and move on; the system's job is to eliminate the retyping, not the review.
What we build
Invoice and receipt extraction, line items, totals, and vendor terms pulled and reconciled against purchase orders.
Contract data extraction, key terms, dates, and obligations identified across formats and made searchable.
Document digitization, scanned and paper records converted into structured, queryable data.
Data validation and reconciliation, extracted values checked against source systems before anything is written.
Structured routing to systems of record, output delivered directly into the ERP, CRM, or accounting platform already in use.
Signalward is equally active in accounts payable pipelines and in contract repositories, working across formats from clean digital invoices to handwritten intake forms and scanned archives. Each build is scoped to document volume and format variability, tested against a sample of the client's own records, and delivered to run against the systems already in place. Underneath, extraction runs on a two-stage pattern: a model reads the document and proposes values, then a deterministic layer checks each one against rules specific to the document type (does this total match its line items, does this date fall inside the policy period) before anything is written to a system of record. Where a value fails that check, it's held for review instead of displayed. The same pattern extends to validation against whatever system the output feeds, so nothing enters a clean system half-checked.
PolicyIQ is the clearest example: carrier insurance documents read into a structured ontology across sixteen lines of business, independently audited on real client policies at 96 to 100 percent displayed-value accuracy, backed by more than two thousand automated tests. Every value shown carries a citation back to the exact passage it came from.
See it in production: PolicyIQ
Related reading: How document extraction accuracy is actually measured
Signalward works with operators across Georgia. See AI automation services in Athens, GA and AI automation services in Atlanta, GA.
How it works
Process audit
Document types, volume, and current handling reviewed: where records originate, where they're meant to end up, and what's lost in between.
Build plan
Extraction targets and validation rules defined for each document type, mapped to the systems set to receive the output.
Build & test
Extraction and validation logic built, then run against a sample of real documents until exceptions are the rare case, not the norm.
Launch & maintain
Pipeline moved into production with monitoring for accuracy and format drift, adjusted as new document types and sources appear.
Questions
How accurate is the extraction?
It depends on the document type, which is exactly the question worth asking before trusting any vendor's number. PolicyIQ, Signalward's insurance comparison build, is independently audited on real client policies at 96 to 100 percent displayed-value accuracy, with a frozen golden set that re-scores every release.
What happens to a value the system isn't sure about?
It's held for a person to check rather than guessed. Nothing displays that the source document doesn't literally say.
What do you need from our team?
A representative sample of the actual documents you process, exceptions and messy scans included. Clean examples alone won't reveal the extraction gaps that matter.
How does pricing work?
Pricing follows scope, not a fixed rate card. The free Process Audit gives you a real number before any work begins.
Start a build
Let's discuss your document workflow.
First conversation strictly confidential. We respond within one business day.