Feature · Document Processing Automation

Document processing automation that reads the inbound and routes to the record.

Document processing automation is the inbound side of document work: a signed contract arrives as a PDF, a vendor invoice lands in an inbox, a customer purchase order drops into a shared drive, a scanned legal filing comes back from the regulator. Document generation is the outbound side. Processing is the inbound side. Strkr runs the processing lifecycle natively: Flows trigger on inbound events, Strkr AI runs OCR and structured extraction, the typed record match pulls the right account or opportunity, the extraction lands as structured fields on the CRM record, and the audit log carries the full chain from inbound receipt through structured write.

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What document processing automation covers

Seven inbound jobs the processing engine has to handle.

Document processing automation covers the inbound side: documents arrive, get read, get matched to records, and update the CRM. The seven jobs below are the processing lifecycle. A team buying processing automation should verify each job runs on the typed record rather than a bolted-on OCR tool that returns JSON and leaves the integration as homework.

Inbound capture

Email, upload, API, shared drive.

The processing engine captures inbound documents from multiple channels: email forwards to a tenant address, direct upload through the CRM UI, API submission from external systems, and shared drive polling (SharePoint, Dropbox, Google Drive). Flows trigger on the inbound event regardless of channel.

OCR extraction

Strkr AI reads the document.

Strkr AI runs OCR and structured field extraction on inbound documents. The engine handles typed documents (digital PDFs with embedded text), scanned documents (image-only PDFs), and image uploads (JPEG, PNG). The structured fields come back typed: dates as dates, amounts as decimals, line items as arrays.

Record match

Pull the right account or opportunity.

The record match engine identifies the right CRM record from the extracted fields: account name, legal entity, PO reference, invoice ID. Fuzzy matching handles minor spelling variations. The common failure where an inbound PO lands on the wrong account because the match was a text contains query does not happen because the match uses typed fields.

Structured write

Extracted fields land on the record.

Extracted fields write to the matched record as structured data: the signed amount on the opportunity, the PO number on the account, the line items on the invoice. The structured write respects the schema. Fields that fail validation flag for human review rather than silently writing a bad value.

Human review queue

Low-confidence extractions escalate.

The extraction engine emits a confidence score per field. Fields below the confidence threshold route to a human review queue for the responsible user to confirm or correct. The common failure where OCR writes a bad value silently and nobody catches it until a customer complains does not happen because the review queue surfaces uncertainty.

Document attach

The original lands on the record.

The original document attaches to the matched record with the version chain and the extraction metadata. The DMS layer applies retention and permission inheritance. The common failure where an inbound document gets processed and the original disappears into a background job queue does not happen because the artifact lives on the account.

Audit chain

Receipt through structured write.

The audit log carries the inbound receipt, the OCR extraction, the record match, the structured write, and any human review resolution. A compliance query like "every inbound PO processed in Q3 that required human review" is a filter on the audit page. The chain is complete from receipt through final state.

The document types that land first

Six inbound document shapes teams automate on day one.

Teams buying document processing automation usually have a shortlist of inbound document types that have hurt enough to justify the automation. The six below show up on almost every shortlist. Each one maps to a prebuilt extraction template that a new team clones and tunes rather than building from scratch.

Countersigned contracts

Signed MSA back from the customer.

The countersigned contract arrives from the customer or the e-signature callback. The extraction engine pulls the signed amount, the signing date, the signer identity, and any marked-up clauses. The structured write updates the opportunity status to closed-signed. The signed PDF attaches to the account.

Customer POs

Purchase orders tied to an opportunity.

Customer POs arrive via email or portal. The extraction engine pulls the PO number, the dollar amount, the line items, and the referenced opportunity. The structured write lands the PO reference on the opportunity. The accounting team sees the PO on the deal rather than hunting through email.

Vendor invoices

AP automation from inbound invoices.

Vendor invoices arrive via email forward or AP mailbox polling. The extraction engine pulls the vendor name, the amount, the due date, the GL coding hints, and the line items. The structured write creates the AP record. Human review fires for vendors below the confidence threshold or for amounts above the auto-approve ceiling.

Expense receipts

Reps submit, extraction categorizes.

Reps forward expense receipts to the tenant expense address. The extraction engine pulls the merchant, amount, date, and category hints. The structured write creates the expense record on the rep. Policy violations (amount above the policy ceiling, non-reimbursable category) flag before the submission reaches the manager.

Regulatory filings

Compliance documents back from authorities.

Regulatory filings (tax notices, filing receipts, agency correspondence) arrive via email forward or paper mail scan. The extraction engine pulls the filing reference, the deadline, the required action, and the associated entity. The structured write creates the compliance task on the matched legal entity.

Partner reports

Monthly commission statements.

Partner reports (reseller commission statements, affiliate payout reports) arrive monthly. The extraction engine pulls the partner, the period, the deal references, and the amounts. The structured write reconciles against the Strkr opportunity data. Variance alerts fire on amounts that do not match the Strkr record.

The engineering underneath

What keeps document processing automation holding up at scale.

Processing automation that works on the first inbound is a demo. Processing that works on the ten thousandth inbound survives drifting document templates, new vendor formats, scanned-paper quality variance, OCR edge cases, and compliance audits. Strkr ships the primitives that keep the automation holding.

OCR engine

Strkr AI with typed field schema.

Strkr AI runs OCR with a typed field schema. The engine does not return raw text and leave parsing as homework; the extraction returns fields matching the configured schema. The common failure where OCR returns a wall of text and someone has to regex it into structured data does not happen.

Template-aware extraction

Known vendors, known formats.

The extraction engine recognizes known document templates (specific vendor invoice layouts, standard PO formats) and applies the template-specific extraction logic. The common failure where every variant of the same template needs a new OCR config does not happen because templates share logic.

Confidence scoring

Low-confidence routes to review.

Every extracted field carries a confidence score. Fields below the configured threshold route to the human review queue. The threshold is configurable per document type and per field. The common failure where OCR writes a bad value silently and nobody catches it does not happen because uncertainty surfaces.

Record match

Typed match with fuzzy fallback.

Record match uses typed field comparison first (exact match on PO number, legal entity, invoice ID), with fuzzy matching as fallback for minor spelling variations. The audit log records which match method succeeded. The common failure where an inbound lands on the wrong account because the match was a loose text contains query does not happen.

Human review UI

Confirm or correct in context.

The human review UI shows the original document alongside the extracted fields. The reviewer confirms or corrects each uncertain field. The corrections feed back into the extraction model for the template. The review happens in context rather than in a disconnected queue tool.

Audit integration

Every processing step emits events.

Every processing step (inbound receipt, OCR extraction, record match, structured write, human review resolution) emits a structured audit event. The audit log is append-only with cryptographic integrity. A compliance query joins processing events with CRM record events in one filter.

Where teams land in practice

Three deployment patterns from live customers.

A 60-seat SaaS, a 180-seat professional services firm, and a 420-seat healthcare company run document processing automation across different inbound shapes. The platform plumbing stays the same. The document types and the review patterns flex to the business. The compliance posture holds.

SaaS pattern

Countersigned contracts and customer POs.

A 60-seat SaaS processes countersigned contracts coming back from DocuSign and customer POs arriving via portal. The extraction fires on inbound. The record match lands the signed amount on the opportunity. The ops lead reviews the human review queue daily. The common failure where a signed contract takes a week to update the pipeline does not happen.

Services firm

POs, invoices, delivery acceptance.

A 180-seat professional services firm processes customer POs on new engagements, vendor invoices on subcontractors, and delivery acceptance letters at project milestones. The extraction and record match handle all three on one surface. The audit chain carries from inbound receipt through the project record.

Healthcare company

Regulatory filings and BAA receipts.

A 420-seat healthcare company processes regulatory filings (CMS correspondence, state agency notices) and BAA countersign receipts. The extraction pulls the filing reference and the deadline. The structured write creates the compliance task on the matched entity. Strkr is HIPAA compliant with a BAA available. SOC 2 Type II is in progress through 2026-Q4.

Daily review cadence

Ops lead resolves the review queue.

The ops lead reviews the human review queue once a day, typically in under thirty minutes. Low-confidence extractions get confirmed or corrected. Corrections feed back into the template-specific extraction logic. The queue stays short because the extraction accuracy improves over time.

Monthly template audit

New templates added for new vendors.

Monthly, the ops lead reviews the extraction performance per document template. Templates with declining accuracy get tuned. New vendor formats get added as templates. The common failure where OCR performance silently degrades for a quarter does not happen because the review is scheduled.

Compliance walkthrough

Audit log satisfies the regulator.

When a regulator asks about an inbound document from six months ago (what did the extraction return, who reviewed low-confidence fields, when did the structured write hit the record), the audit log carries the full chain. The answer is a URL with filters, not a six-week engagement.

Document processing automation that lands inbound on the right record.

Strkr runs the inbound processing lifecycle on the typed CRM record. Multi-channel capture, Strkr AI OCR and extraction, typed record match, structured write with human review for low confidence, DMS attach, audit chain. Flat $50 per seat, no per-document fee.

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Common questions

What buyers ask about this feature.

What is document processing automation and how does it differ from document automation?

Document automation covers the outbound side: generating documents from templates and typed records. Document processing automation covers the inbound side: a document arrives, gets read, gets matched to the right record, and lands as structured data on the CRM. Strkr runs both sides natively. The drafter generates outbound documents (proposals, contracts, SOWs). The processing engine handles inbound documents (countersigned contracts, customer POs, vendor invoices, regulatory filings). Both sides share the same typed record read, the same audit log, and the same permission model. The flat seat price covers both capabilities with no per-document fee on either side.

What inbound document types can Strkr process?

The prebuilt extraction templates cover the common revenue-adjacent inbound types: countersigned contracts coming back from e-signature, customer purchase orders arriving via email or portal, vendor invoices for AP automation, expense receipts submitted by reps, regulatory filings from agencies, and partner reports (reseller commission statements, affiliate payout reports). New document types get added as templates through the admin UI without engineering. The template authoring surface is designed for the ops lead rather than for a developer.

How does Strkr handle low-confidence OCR extractions?

Every extracted field carries a confidence score from Strkr AI. Fields below the configured threshold route to a human review queue for the responsible user to confirm or correct. The threshold is configurable per document type and per field. The reviewer sees the original document alongside the extracted fields in a review UI. Corrections feed back into the template-specific extraction logic for the next inbound. The common failure where OCR writes a bad value silently and nobody catches it until a customer complains does not happen because uncertainty surfaces at the review queue rather than at the customer complaint.

Does Strkr document processing automation include OCR for scanned paper documents?

Yes. Strkr AI handles typed documents (digital PDFs with embedded text), scanned documents (image-only PDFs with no embedded text), and image uploads (JPEG, PNG, HEIC). The OCR engine runs on scanned documents and image uploads with the same structured extraction logic as typed documents. Scanned-paper quality varies, so the confidence scoring applies more aggressively to low-quality scans and routes more fields to human review. The common failure where OCR silently returns garbled fields on a low-quality scan does not happen because the confidence threshold catches it.

How does Strkr match an inbound document to the right CRM record?

The record match engine uses typed field comparison first: exact match on PO number, legal entity name, invoice ID, opportunity ID. Fuzzy matching handles minor spelling variations as fallback. The match considers the extraction confidence and the field uniqueness. The audit log records which match method succeeded. If no confident match lands, the inbound routes to a human review queue for the responsible user to pick the right record. The common failure where an inbound PO lands on the wrong account because the match was a loose text contains query does not happen because the match uses typed field comparison.

Is document processing automation priced separately from document automation?

No. Strkr is a flat $50 per seat for the full platform: CRM, document automation (outbound generation), document processing automation (inbound extraction), document management system, audit log, Flows, Products module quoting, and Strkr AI. No per-document fee on either side. No per-page OCR fee. No per-extraction fee. A team that processes 10,000 inbound documents a month pays the same as a team that processes 100. The common failure where a vendor charges per inbound document and the finance team starts rate-limiting the ops team on processing economics does not happen on this pricing model.

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