How to Build a Document Extraction Agent on StackAI: Step-by-Step Guide for Accurate PDF Data Extraction
How to Build a Document Extraction Agent on StackAI (Step-by-Step Tutorial)
If you’ve ever tried to build a document extraction agent on StackAI, you already know the hardest part isn’t getting an LLM to read a PDF. It’s getting reliable, structured data out of messy real-world documents and pushing that data into the systems your team actually uses.
In this guide, you’ll learn how to build a document extraction agent on StackAI end-to-end: ingestion, OCR for PDFs, schema design, LLM document parsing, validation and exception handling, and finally webhook or API export (plus practical options like Sheets and databases). The goal is a production-ready document extraction workflow that’s accurate, maintainable, and easy to iterate on as templates change.
What a Document Extraction Agent Is (and Why It Matters)
A document extraction agent is an automated workflow that turns unstructured documents (PDFs, scans, images) into structured data (usually JSON) that downstream systems can consume.
A production-grade document extraction agent typically does five things:
Ingests documents (upload, email, drive folder, API/webhook)
Runs OCR and text normalization when needed
Extracts fields into a structured schema (JSON schema extraction)
Validates outputs and routes exceptions for review
Exports the results to business systems (Sheets, CRM, DB, webhook / API export)
This matters because most operational work is still buried in PDFs: invoices, contracts, onboarding packets, insurance forms, and compliance documentation. When extraction is reliable, you can move faster without sacrificing controls.
Common use cases for document extraction
Teams usually start with a narrow, high-volume workflow where accuracy has obvious business value, like:
Invoice extraction for AP: vendor, invoice number, due date, totals, line items
Contract data extraction: parties, renewal dates, fees, key clauses
Claims and insurance forms: policy numbers, claimant info, diagnosis/procedure codes
KYC/onboarding: IDs, proof of address, business registrations
Where extraction typically fails
Most PDF data extraction projects break down for predictable reasons:
Poor scans, skewed pages, faint text, or multi-page documents
Tables and line items that get merged or dropped
Template drift (the vendor updates layout and fields move)
No validation layer (wrong totals slip through)
No exception handling (every edge case becomes a manual fire drill)
The rest of this tutorial is designed to prevent those failure modes from day one.
Before You Start: What You’ll Build (Architecture + Example Output)
To keep this practical, the running example here is invoice extraction. Invoices are perfect for learning because they combine messy layouts with clear validation rules (numbers should add up, due dates should be after invoice dates, currencies should be consistent).
What the finished agent does
Your StackAI document extraction agent will:
Accept a PDF or image (scanned or digital)
Run OCR for PDFs when needed (especially scanned PDFs)
Extract invoice fields into a consistent JSON output
Validate the output (schema + business rules)
Export the result to your system of choice (webhook/API is the most flexible)
Example target JSON output
This is the shape you want before you touch prompts. Schema-first design is how you get consistent structured data extraction across varying templates.
Why schema-first wins
When you define fields upfront, three things get easier immediately:
Prompting becomes more precise because the model isn’t guessing the shape of the answer.
Validation becomes straightforward (types, required fields, allowed formats).
Exports become reliable because downstream mapping doesn’t change every time.
This is the difference between a demo and a document extraction workflow your finance team can trust.
Step 1 — Set Up Your StackAI Project and Agent Workflow
In StackAI, you’ll want to structure your agent like a pipeline rather than a single monolithic step. Teams get better reliability when they break work into small stages with clear inputs and outputs.
Recommended workflow structure
Use a modular flow that mirrors how humans actually process documents:
Ingestion
OCR + text preparation
Extraction (LLM document parsing into schema)
Validation + exception handling (human-in-the-loop review when needed)
Export (webhook / API export, Sheets, DB, etc.)
This approach is especially important if you’re building multiple agents over time. In enterprise settings, the highest-performing initiatives avoid “do everything” agents and instead build targeted workflows with clear inputs and outputs, then scale from there.
Versioning and naming
Adopt conventions early so you can maintain and audit changes:
Agent name: invoice_extraction_v1
Prompt version: prompt_v3_line_items_fix
Schema version: invoice_schema_v2
Validation version: validation_rules_v1
Even a simple naming standard prevents painful confusion later when accuracy changes after a prompt tweak.
Build a small test dataset
Start with 5–10 documents. Make sure they’re intentionally varied:
A clean digital PDF (easy baseline)
A scanned PDF with skew or low contrast
A multi-page invoice
A long line-item table
One invoice with discounts or shipping lines
A non-USD currency example (even if you don’t plan to support it yet)
This dataset becomes your regression suite for Step 8.
Step 2 — Ingest Documents (PDFs, Images, Email, or Upload)
Your ingestion choice depends on whether you’re testing or going live.
Ingestion options
For most teams, the progression looks like this:
Manual upload for development and testing
Drive folder or shared inbox for production intake
Webhook/API for integration into an internal app or portal
If you’re planning to build a document extraction agent on StackAI for real operations, webhook-based ingestion is usually the cleanest because it’s deterministic and easier to secure.
Capture metadata at ingestion
The file alone is rarely enough. Add metadata so downstream actions are traceable:
source (email, drive, portal, API)
received_at timestamp
uploader or sender identity (if applicable)
customer_id / vendor_id / property_id (whatever matters to your workflow)
document_type (if known, or leave for a classifier later)
Good metadata makes exception handling far easier because reviewers can route issues back to the right owner.
Pre-processing tips that dramatically improve accuracy
A few small steps can improve OCR and extraction more than any clever prompt:
Convert images to a consistent format (PDF or PNG)
Deskew and rotate pages before OCR
Split extremely large PDFs (especially if they contain multiple documents)
Avoid overly aggressive compression that destroys text edges
Document ingestion checklist
Ensure correct orientation (no sideways scans)
Avoid cropped margins (totals often live in corners)
Keep original file name (useful for audit trails)
Store document source metadata alongside the file
Step 3 — OCR and Text Preparation (Make Messy Docs Usable)
OCR is where most document extraction workflows either become stable or fragile. It’s also where many tutorials cut corners.
When OCR is required
Scanned PDFs: OCR is required (the “text layer” is basically an image)
Digital PDFs: OCR may not be required, but you still need text extraction and normalization
Photos of documents: OCR is required and quality varies widely
A robust workflow detects whether the document has extractable text before defaulting to OCR. That keeps costs down and reduces noise.
OCR best practices for real documents
The big decision is whether to preserve layout.
Plain text OCR is simpler for the LLM, but tables and columns may collapse.
Layout-aware OCR is better for invoices and statements, but can introduce artifacts like repeated headers.
In invoice extraction, layout often matters because line items rely on row structure. If line items are critical, prioritize table-aware or layout-preserving OCR.
Text normalization that improves LLM extraction
After OCR, normalize the text before sending it to the model:
Remove repeated headers/footers that appear on every page
Join hyphenated words broken across lines
Normalize whitespace (collapse excessive spacing)
Preserve page boundaries when multi-page context matters
OCR quality gating (don’t skip this)
Add a quick check before you attempt extraction:
If extracted text length is suspiciously low, OCR likely failed
If most characters are non-alphanumeric, you may have encoding noise
If the document language is unexpected, route to review
If confidence is low, don’t “force extraction” — escalate
A simple rule like “if text < 500 characters for a 2-page invoice, route to review” can save hours of debugging later.
Step 4 — Define the Extraction Schema (Fields, Types, Rules)
Schema design is where you translate business requirements into something the agent can reliably produce and validate.
Start with business requirements
Define:
Required fields: invoice_number, vendor_name, total, invoice_date
Optional fields: PO number, remit_to_address, notes
Acceptance criteria: totals must reconcile within a tolerance, dates must be valid
This prevents the common failure where the agent returns plausible-looking JSON that can’t actually be used.
A practical invoice extraction schema
Use snake_case and predictable types. Keep formats strict.
vendor_name: string
invoice_number: string
invoice_date: string (ISO 8601: YYYY-MM-DD)
due_date: string (ISO 8601) or null
currency: string (ISO 4217 like USD, EUR)
subtotal: number or null
tax: number or null
total: number
line_items: array of objects
notes: string or null
For line items, keep it minimal at first: description, quantity, unit_price, line_total. You can expand later with SKU, tax category, or service dates.
Add field-level descriptions
Field descriptions do more than help humans. They improve extraction reliability because the model has less ambiguity. For example:
invoice_number: “Unique invoice identifier as printed on the invoice. Do not invent one.”
total: “Total amount due including tax and fees. Prefer ‘Amount Due’ if present.”
Define constraints and rules
Use constraints to reduce garbage outputs:
Dates must be ISO 8601
Currency must be one of your supported codes
Numbers must be numeric (no “$1,234.00” strings)
If a value is missing, return null instead of guessing
Plan for multi-entity extraction early, even if you don’t support it yet. For example, some PDFs contain multiple invoices in one file.
Step 5 — Build the Extraction Prompt (Reliable, Structured Outputs)
A good extraction prompt is direct, strict about formatting, and explicit about what not to do. The goal is consistent JSON schema extraction, not a narrative summary.
Prompt template (copy/paste)
Use a structure like this and adapt the schema section to your fields:
Few-shot examples
If you want to push accuracy quickly, add two short examples:
One clean invoice with obvious totals
One messy invoice where the “total” appears as “Balance Due” or where tax is included
Keep examples short and focused on edge cases. You’re teaching formatting and decision rules, not summarization.
Common prompt pitfalls to avoid
Vague field definitions (“total” without specifying whether it’s subtotal or amount due)
No instruction for missing values (models fill gaps)
No numeric formatting rules (you get “$1,382.40” as a string)
No guidance for tables (line items collapse into one blob)
If you plan to build a document extraction agent on StackAI that’s stable over time, the prompt should read like a contract: explicit, testable, and hard to misinterpret.
Step 6 — Add Validation, Error Handling, and Human-in-the-Loop
Extraction is not the finish line. Validation is what makes this safe enough for finance, legal, or compliance workflows.
Validation layer 1: schema validation
Schema validation checks:
Types are correct (numbers are numbers, arrays are arrays)
Required fields exist (invoice_number, vendor_name, total)
Date strings match expected format
If schema validation fails, you can automatically re-run with a stricter prompt, or route the document to review.
Validation layer 2: business rule validation
Business rules catch “looks right but is wrong” outputs:
subtotal + tax ≈ total (use a small tolerance like 0.01–0.05 depending on rounding)
invoice_date ≤ due_date (if both present)
total > 0
If line_items exist, sum(line_total) ≈ subtotal (optional but powerful)
These rules are also great for exception routing because they produce clear error messages.
Validation rules checklist
Required fields present: vendor_name, invoice_number, total
Date format correct and logically consistent
Totals reconcile within tolerance
Currency consistent across amounts (or explicitly null)
Line items extracted as separate entries when present
Confidence scoring and gating
Even without an explicit confidence score from each step, you can derive useful signals:
Missing invoice_number or total is a hard fail
OCR text quality low is a hard fail
Totals not reconciling is a soft fail that should trigger review
Too many nulls is a warning sign
Human-in-the-loop review (what it should look like)
Human-in-the-loop review works best when reviewers see:
The original PDF
The extracted JSON
The specific fields that failed validation
The text snippet where the field was found (or where it should have been found)
Most importantly, corrections should feed back into iteration: update the schema descriptions, add an example, or adjust OCR settings.
Step 7 — Export the Extracted Data (Sheets, CRM, DB, Webhook/API)
Once your agent outputs validated JSON, you can connect it to almost anything. This is where document extraction becomes operational automation.
Common export targets
Webhook / API export to an internal service
Google Sheets row (quick ops workflows)
Airtable or Notion database (lightweight tracking)
Postgres insert (reporting and audit trails)
CRM or ERP integration via middleware
Webhook export is usually the best default because it keeps your integration logic in your application, where you can handle retries, deduplication, and authentication cleanly.
Mapping tips (especially for line items)
Line items are nested arrays, which some destinations don’t handle well. A practical approach is:
Store invoice-level fields in one record
Store line_items as a separate list in a second system/table
Or store line_items as JSON in a single field if your DB supports it
Also store both:
raw_text (from OCR/text extraction)
extracted_json (the structured output)
This is essential for auditability and debugging.
Idempotency and deduplication
Documents get re-uploaded. Emails get forwarded. Webhooks get retried. Build deduplication into your workflow:
Use (vendor_name + invoice_number + invoice_date) as a natural key when available
If invoice_number is missing, generate a file hash of the PDF and use that as a fallback
Keep a processing log with statuses: received, extracted, validated, exported, failed_review
What to log for traceability
For production-grade PDF data extraction, log:
Document ID and source metadata
Extraction timestamp
Model version / configuration
Prompt version
Schema version
Validation results (pass/fail + which rules failed)
This makes changes debuggable and helps explain accuracy shifts.
Step 8 — Test and Iterate (Evaluation for Accuracy and Drift)
If you want this to stay reliable, you need lightweight evaluation. Otherwise, each improvement attempt becomes guesswork.
Build a test set that reflects reality
Keep your original 5–10 docs, then expand:
20–50 documents once you’re serious about production
Include new vendor templates as they appear
Keep a few “nightmare docs” on purpose
Measure accuracy in a way that matches business value
Track at least these three metrics:
Field-level accuracy: percent of fields that are correct
Document-level success rate: percent of documents that pass validation without review
Critical field pass rate: invoice_number, vendor_name, total, due_date
Critical field pass rate is often the best early metric because it’s directly tied to whether the workflow is usable.
Regression testing
When you update OCR settings, change the prompt, or revise the schema:
Re-run the full test set
Compare results against “golden” outputs
Make sure you didn’t fix one vendor and break three others
Handling template drift
Template drift is inevitable. The best mitigation strategies:
Add new examples to your prompt or evaluation set
Improve schema descriptions for ambiguous fields
Add a routing step that classifies document types (invoice vs receipt vs statement)
If you have a few dominant templates, consider specialized prompts per template
This is how teams scale from one successful workflow to many without compounding risk.
Best Practices for Production-Grade Document Extraction
Once you’ve built the baseline, these practices are what make it durable.
Choose the right approach per document type
Template-heavy workflows: combine rules with targeted extraction and strict validation
Template-light workflows: rely on LLM document parsing with strong schema constraints and review gating
Cost and performance controls
Cache OCR outputs so you don’t re-OCR the same file during iteration
Only run heavy extraction when OCR quality passes basic checks
Consider a two-pass approach:
Security and compliance basics
Document workflows often include PII, banking details, or contract terms. Build basic controls early:
Restrict access to documents and outputs by role
Define retention policies (don’t keep everything forever by default)
Redact sensitive fields when exporting to lower-trust systems
Maintain audit logs for review and compliance needs
Observability and maintenance cadence
Monitor failure rates by vendor/template
Track which validation rules fail most often (these point to prompt/schema gaps)
Review failed docs monthly and update prompts/examples accordingly
Troubleshooting Guide (Common Issues + Fixes)
Output isn’t valid JSON
Cause: the prompt allows extra text, or the model is being “helpful.”
Fix:
Add a strict “Return only JSON” rule
Enforce schema validation and auto-retry with a stricter prompt
Remove any instruction that invites explanations
Totals are wrong or tax doesn’t reconcile
Cause: the model chose subtotal instead of total, or misread a “Balance Due.”
Fix:
Add explicit definitions: total = amount due including tax
Add business rule validation and route mismatches to review
If needed, run a second pass that explicitly re-checks totals
Line items are missing or merged
Cause: OCR collapsed a table into text blocks.
Fix:
Use layout-aware OCR settings when possible
Add line-item extraction rules: “each row becomes one array item”
Consider a dedicated line-item extraction step separate from header fields
Dates and currencies are inconsistent
Cause: formatting drift across documents.
Fix:
Lock ISO date formatting and currency code rules
Add normalization (strip symbols, parse formats)
Keep a “raw_value” field only if you truly need it
OCR returns gibberish
Cause: low-quality scan, compression artifacts, or wrong language settings.
Fix:
Improve input quality (deskew, increase contrast)
Re-run OCR with different settings
Fail fast and route to human-in-the-loop review instead of forcing extraction
Conclusion + Next Steps
To build a document extraction agent on StackAI that actually holds up in production, focus on the full pipeline, not just the extraction step. The reliable path is:
Ingest → OCR → schema-first extraction → validation → exception handling → export
Once this baseline is working, the most valuable next upgrades are:
Add a document type classifier/router (invoice vs receipt vs statement)
Build a lightweight review queue for exceptions
Maintain an evaluation set and run regression tests after every change
If you want to see what a production-grade document extraction workflow looks like in your environment, book a StackAI demo: https://www.stack-ai.com/demo
