I practice Canadian immigration law and have a basic understanding of programming and system workflows, though I am not a technical expert. I am exploring the idea of building an internal application for my team that can review immigration application files to identify inconsistencies, errors, and potential issues, and then generate an audit-style report.
In addition, the application would be able to draft a strong submission letter for an immigration officer, tailored to the specific type of application.
Proposed functionality:
My team would upload a complete immigration application folder containing PDF forms and supporting documents such as passports, birth certificates, employment records, and other relevant materials. During the upload process, we would specify the type of immigration application (for example, study permit, work permit, etc.).
The system would then:
Read and extract data from PDF and image files (JPG/PNG).
Cross-check key information across all documents (for example, passport numbers, names, dates of birth).
Identify inconsistencies, errors, or missing information.
Provide suggestions to strengthen the application.
Generate a downloadable PDF report listing all findings.
Draft a customized submission letter explaining the applicant’s purpose and strengthening their case for the selected immigration category.
Is it feasible to build an application with this level of functionality using current AI technologies with no coding?
I would appreciate any guidance or insights on feasibility, architecture, and best practices for building such a system.
Yes it’s possible but not just with Bubble. Extracting data from PDF for example will need a third party provider that can be cakled using API connector
Yes, this is largely feasible, but it requires significant low-code work, primarily in setting up and managing API connections to external AI services. It is not a “no-code” project in the strictest sense due to the technical nature of API integrations.
The core challenge is that Bubble has no native ability to read files (OCR) or perform intelligent analysis. You must orchestrate several external services.
Recently i build an application for local contractor for water supply with tankers. He wanted a system when at the end of week his fleet driver submit a manual log of loading and un loading of water.
We use Gemeni-flash 2.0 which very cheap and quate good understanding human written text. To extract log detail and build a table which his employees then with just a button record in data base.
Yes, it’s feasible with current AI, but you’ll still need a lightweight build: upload intake, OCR for scans, PDF text extraction, a rules layer for cross checks, and an LLM layer to summarize issues and draft the submission letter. No code tools can handle the whole pipeline perfectly end to end, so plan for human review, clear disclaimers, and a feedback loop to improve accuracy, especially for low quality scans and edge cases.
I recommend integrating claude into your API connector (or some other AI model), this way you will be able to upload the image and get the results.
If you don’t want (or can’t) use API models, you can create your own plugin by integrating some library or machine learning service, like TensorFlow.js, to interpret the image and parse it to get the data you need. Requests will be free, but it will be heavier and slower for the user than an API.
Hi! To complete this, the first step is to extract the data from the PDF (especially if it’s a scanned PDF) and from any images. That extracted content then needs to be passed as context into an agentic AI workflow, such as n8n or the OpenAI Agent Builder (currently in beta).
OpenAI Agent Builder is generally easier to get started with, while n8n gives you more control and flexibility if you need advanced workflows.
For extracting data from PDFs and images, you can take a look at these templates/plugins:
