This year’s API World was packed with energy—and it was amazing meeting so many developers face-to-face at the Foxit booth. We spent three days trading ideas about document automation, AI workflows, and integration challenges.
Our team hosted a hands-on workshop and sponsored the API World Hackathon, where developers submitted 16 high-quality projects built with Foxit APIs. Submissions ranged from:
Automated legal-advice generators
Compatibility-rating apps that analyze your personality match
AI-powered resume optimizers that tailor your CV to dream-job descriptions
Collaborative doodle games that turn drawings into shareable PDFs
Each project offered a new perspective on what’s possible with Foxit APIs—and we loved seeing the creativity.
Among all the sessions, Jorge Euceda’s workshop stood out as a crowd favorite. It showed how to make AI document decisions auditable, explainable, and replayable using event sourcing and two key Foxit APIs. That’s exactly what we’ll walk through below.
Click here to grab the project overview file.
Prefer to follow along with the live session instead of reading step-by-step?
Watch Jorge’s complete “AI-Powered Resume to Report” presentation from API World 2025.
It includes every step shown below—plus real-time API responses.
What You’ll Build
A complete, auditable workflow:
Resume Upload → Extract Resume Data → AI Candidate Scoring → Generate HR Report → Event Store
This workshop is designed for technical professionals and managers who want to learn how to use application programming interfaces (APIs) and explore how AI can enhance document workflows. Attendees will get hands-on experience with Foxit’s PDF Services (extraction/OCR) and Document Generation APIs, and see how event sourcing turns AI decisions into an auditable, replayable ledger.
By the end, you’ll have a Python-based demo that extracts data from a PDF resume, analyzes it against a policy, and generates a polished HR Report PDF with a traceable event log.
Getting Set Up
To follow along, you’ll need:
Access to a terminal with a Python 3.9+ Environment and internet connectivity
Visual Studio Code or your preferred IDE
Basic familiarity with REST/JSON (helpful but not required)
- Install Dependencies
python -V
# virtual environment setup, requests installation
python3 -m venv myenv
source myenv/bin/activate
pip3 install requests- Download the project’s zip file below
Now extract the files somewhere in your computer, open in Visual Studio Code or your preferred IDE.
You may use any sample resume PDF for inputs/input_resume.pdf. A sample one is provided, but you may leverage any resume PDF you wish to generate a report on.
- Create a Foxit Account for credentials
Create a Free Developer Account now or navigate to our getting started guide, which will go over how to create a free trial.
Hands-On Walkthrough
Step 1 – Open the Project
Now that you’ve downloaded the workshop source code, navigate to the resume_to_report.py file, which will serve as our main entry point.
Once dependencies are installed and the ZIP file extracted, open your workspace and run:
python3 resume_to_report.pyYou should see console logs showing:
An AI Report printed as JSON
A generated PDF (
outputs/HR_Report.pdf)An event ledger (
outputs/events.json) with traceable actions
Step 2 — Inspect the outputs
Open the generated HR report to review:
Candidate name and phone
Overall fit score
Matching skills & gaps
Summary and policy reference in the footer
Then open events.json to see your audit trail—each entry captures the AI’s decision context.
{
"eventType": "DecisionProposed",
"traceId": "8d1e4df6-8ac9-4f31-9b3a-841d715c2b1c",
"payload": {
"fitScore": 82,
"policyRef": "EvaluationPolicy#v1.0"
}
}This is your audit trail.
Step 3 — Replay & Explain a Policy Change
Replay demonstrates why event-sourcing matters:
Edit
inputs/evaluation_policy.json: add a hard requirement (e.g.,"kubernetes") or adjust the job_description emphasis.Re-run the script with the same resume.
Compare:
New decision and updated PDF content
Event log now reflects the updated rationale (
PolicyLoadedsnapshot → newDecisionProposedwith the sametraceIdlineage)
Emphasize: The input resume hasn’t changed; only policy did — the event ledger explains the difference.
Policy: Drive Auditable & Replayable Decisions
The AI assistant uses a JSON policy file to control how it scores, caps, and summarizes results. Every policy snapshot is logged as its own event, creating a replayable audit trail for governance and compliance.
{
"policyId": "EvaluationPolicy#v1.0",
"job_description": "Looking for a software engineer with expertise in C++, Python, and AWS cloud services. Experience building scalable applications in agile teams; familiarity with DevOps and CI/CD.",
"overall_summary": "Make the summary as short as possible",
"hard_requirements": ["C++", "python", "aws"]
}Notes:
policyIdappears in both the report and event log.job_descriptiondefines what the AI is looking for.Changing these values creates a new traceable event.
Generate a Polished Report
Next, use the Foxit Document Generation API to fill your Word template and create a formatted PDF report.
Open inputs/hr_report_template.docx, you will find the following HR reporting template with placeholders for the fields we will be entering:
Tips:
Include lightweight branding (logo/header) to make the generated PDF presentation-ready.
Include a footer with traceable Policy ID and Trace ID Events
Results and Audit Trail
Here’s what the final HR Report PDF looks like:
Every decision has a Trace ID and Policy Ref, so you can recreate the report at any time and verify how the AI arrived there.
Why Event-Sourced AI Matters
This pattern does more than score resumes—it proves that AI decisions can be transparent, deterministic, and trustworthy.
By using Foxit APIs to extract, analyze, and generate documents, developers can bring auditability to any workflow that relies on machine logic.
Key Takeaways
Auditability – Every AI step emits a verifiable event.
Replayability – Change a policy and regenerate for deterministic results.
Explainability – Decisions carry policy and trace references for clear “why.”
Automation – PDF Services and Document Generation handle the document lifecycle end-to-end.
Try It Yourself
Ready to build your own auditable AI workflow?
Demo and Source Code: document-workflows-with-foxit.pages.dev
Foxit Developer Portal: developer-api.foxit.com
API Docs: docs.developer-api.foxit.com
Watch the Full Presentation: Euceda’s API World session
Closing Thought
At API World, we set out to show how Foxit APIs can power real, transparent AI workflows—and the community response was incredible. Whether you’re building for HR, legal, finance, or creative industries, the same pattern applies:
Make your AI explain itself.
Start with the Foxit APIs, experiment with policies, and turn every AI decision into a traceable event that builds trust.
