Invoices, contracts, and forms turned into structured data your systems can actually use, with a confidence score on every extracted field and a review queue for anything the model isn't sure about. Not a pipeline that silently guesses and hopes the number is right.
Document extraction that flags what it isn't sure about
The problem, reframed
Every vendor in this space demos a clean invoice getting extracted perfectly and implies the rest of your document volume will behave the same way. It will not. Scanned receipts with a coffee stain across the total, contracts with a clause structured differently than the last fifty, and forms filled out by hand all extract with genuinely lower confidence, and a pipeline that reports every field as equally certain is lying to whoever reads the output next.
I think the honest engineering choice is building a confidence threshold into the extraction from day one: fields the model is genuinely confident about flow straight through, and fields below that threshold route to a human review queue before they touch your accounting system or your CRM. That is slower to demo than "fully autonomous document processing" and it is the version that does not put a wrong number into your books.
What we build
01
Extraction using a vision-capable LLM, with OCR as a fallback.
Claude API and OpenAI's current models read PDFs and scanned images directly for most document quality, extracting into a defined JSON schema rather than free text, so downstream systems get a predictable structure every time. For low-quality scans where direct vision extraction struggles, a traditional OCR pass feeds cleaner text into the extraction step first.
02
Document classification before extraction.
Not every document in your intake is the same type, and forcing an invoice schema onto a contract produces garbage. We build a classification step that routes each document to the right extraction schema, so a mixed intake of invoices, receipts, and purchase orders gets handled correctly without a human sorting them first.
03
Confidence scoring on every extracted field, not just the document overall.
A document-level confidence score hides the fact that nine fields extracted cleanly and one, usually the total or a date, did not. We score at the field level so a review queue surfaces the one uncertain number instead of an entire document that was actually fine.
04
Business logic validation on top of extraction.
Line items that should sum to the stated total, dates that should fall in a sane range, vendor names that should match your existing records. These checks catch extraction errors a confidence score alone might miss, because a model can be confident and still wrong.
05
Human review queue built into the workflow, not bolted on after.
Low-confidence fields and failed validation checks route to a review interface where a human corrects the value once, and that correction can feed back into refining the extraction prompt for similar documents going forward.
06
Integration into your existing systems.
Extracted, validated data writes into your accounting software, ERP, or CRM through self-hosted n8n, matched to the fields and formats those systems actually expect, rather than dropping a generic CSV export you have to reformat by hand.
Recently shipped
Built for a finance operations team processing 200+ invoices per week. We built a document pipeline that classified incoming PDFs by type, extracted vendor name, invoice number, line items, and total with field-level confidence scoring, and routed anything below threshold to a review queue before writing validated data into their accounting system. A handwritten receipt and a partially scanned invoice that would have gone straight into the books under a naive pipeline both got flagged for review. The team stopped keying clean invoices by hand and kept human eyes on the documents that actually needed them.
Built on a modern, connected stack
Claude API and OpenAI handle the extraction and classification work, Postgres with pgvector stores extracted records and supports retrieval when a downstream feature needs to search across processed documents, and self-hosted n8n orchestrates the flow from intake through validation to writing into your accounting or CRM system. If your process already runs through HubSpot, Zoho, Salesforce, or an accounting platform, extracted data lands there directly instead of in a separate database you now have to maintain. If the pipeline needs a real tool layer between the LLM and your structured data, an MCP server we build slots directly into the same architecture.
Who this is for
01
Finance and operations teams
manually keying invoice or receipt data into an accounting system every week, where the volume justifies automation but the error cost is high enough that full autonomy is the wrong goal.
02
Legal and contracts teams
who need key terms, dates, and parties extracted from a contract library without reading every document by hand.
03
Companies with high-volume document intake,
applications, claims, onboarding forms, where manual data entry is the actual bottleneck on throughput.
04
Agencies
wanting white-label document processing build capacity for a client's back-office automation project.
Why Flowagenz
Code and pipeline you own outright.
Full ownership of the extraction logic, the review interface, and the integration code on completion. No per-document SaaS pricing replacing a system you actually control.
Based in Salem, Tamil Nadu
Western-grade engineering at a rate structure offshore delivery makes possible.
Real overlap, not vague promises.
Async-first communication with working hours that overlap US Eastern mornings, UK afternoons, and Australian business hours on the same day.
We build in the review step most vendors sell around.
A confidence threshold and a human review queue are not a limitation of the technology, they are the responsible way to deploy it against documents that carry real financial or legal weight. We would rather tell you that upfront than sell full autonomy and let you find the gap in production.
How it works
01
01
Document type and volume scoping.
We review sample documents across the types you actually receive, including the messy ones, not just the clean examples, and define the extraction schema for each type.
02
02
Pipeline build.
Classification, extraction, confidence scoring, and validation rules built and tested against your real document samples.
03
03
Review interface and integration.
A review queue for low-confidence fields, wired into your accounting, ERP, or CRM system so validated data writes where it needs to go automatically.
04
04
Tuning against live volume.
A monitored rollout where extraction accuracy and review queue volume get tracked against real throughput, with the confidence threshold adjusted based on actual results rather than a guess made before launch. Typical builds run 3 to 6 weeks depending on document variety and how many systems the pipeline integrates into.
What you get on handover
Every AI document processing pipeline ships with the following:
Full source code for the classification, extraction, validation, and review queue logic
Document classification routing each document type to the correct extraction schema automatically
Field-level confidence scoring on every extracted value, with configurable threshold for human review
Business logic validation rules catching extraction errors a confidence score alone might miss
Human review interface for correcting low-confidence fields, with feedback loop to refine extraction prompts
Integration into your accounting, ERP, or CRM through n8n, writing data in the format those systems expect
Extraction schema documentation for each document type, showing exactly which fields get extracted and how
Confidence threshold tuning guide based on your real document volume and accuracy requirements
Staging and production testing notes documenting accuracy against your sample documents
API key and environment variable list with secure storage guidance
Documentation for your own team to extend document types, adjust validation rules, or modify the review queue
30-day post-launch support for accuracy tuning, threshold adjustment, and integration refinements
Zero dependency on Flowagenz infrastructure; everything lives in your codebase, your database, your control
FAQ
Frequently Asked Questions
Everything you need to know about our process and digital systems.
It depends heavily on document quality and type, and we will not quote a single accuracy number that applies to your entire document mix without seeing real samples first. What we can commit to is the confidence threshold and review queue design that catches uncertain extractions before they reach your systems, rather than a black-box number nobody can verify.
Send your documents for a real accuracy estimate
Send us a sample of the documents you actually process, including the messy ones, and we will scope the pipeline and give you an honest accuracy read on a short call. No generic pitch.