Build a Visa Requirement Checker with n8n & Weaviate

Every trip starts with a dream, but too often it gets blocked by a simple, frustrating question: “Do I need a visa for this?” Hunting through embassy websites, outdated blog posts, and confusing rules steals time and energy from the journey you actually care about.

What if that work could quietly happen in the background, on autopilot, while you focus on building your product, serving your customers, or planning your next big expansion?

In this guide, you will turn that idea into reality. You will build a scalable, automation-friendly Visa Requirement Checker using:

• n8n for orchestration and workflow automation
• Cohere embeddings to convert policy text into searchable vectors
• Weaviate as a vector database to store and retrieve relevant chunks
• Anthropic as the chat model behind an n8n Agent
• Google Sheets for logging, analytics, and continuous improvement

This is more than a one-off tool. It is a stepping stone toward a more automated, focused workflow where your systems do the heavy lifting and you stay free to think strategically.

From manual research to automated clarity

Traditional visa checks often rely on static tables or hardcoded rules. They are brittle, time-consuming to maintain, and easy to break every time regulations shift. When you are trying to grow a product or organization, that kind of manual work quietly drains momentum.

By moving to an LLM-driven, vector-search-based workflow, you unlock a different way of working:

• Fast, context-aware answers directly from your curated source documents
• Flexible knowledge base that grows as you add or update documents
• Automation-ready endpoints via a webhook and n8n flow that can plug into any app
• Transparent logging in Google Sheets so you can review, audit, and improve over time

Instead of chasing rules, you design a system that learns from your data and scales with your ambitions.

Mindset shift: treating automation as a growth partner

This template is not just a clever trick with AI tools. It represents a mindset shift:

• From reactive visa checks to a proactive knowledge system
• From scattered scripts to a single, orchestrated workflow in n8n
• From “I have to remember to check this” to “my system checks it for me”

Once you have one workflow like this in place, it becomes easier to imagine others: eligibility checkers, policy explainers, internal support assistants, and more. The Visa Requirement Checker is your practical starting point.

Solution overview: how the workflow fits together

At the heart of this solution is an n8n workflow that:

1. Receives a user’s travel details through a Webhook
2. Searches a Weaviate vector store filled with Cohere embeddings of visa documents
3. Uses an Anthropic chat model via an n8n Agent to interpret and respond
4. Logs the entire interaction to Google Sheets for analytics and improvement

Key components in the architecture

• Webhook: A public POST endpoint that accepts inputs such as passport country, destination, purpose, and travel dates.
• Text Splitter: Breaks large policy documents into manageable chunks using chunkSize and chunkOverlap settings.
• Cohere Embeddings: Transforms each chunk into a vector representation, ready for semantic search.
• Weaviate Vector Store: Stores those vectors and retrieves the most relevant ones for each user query.
• Agent + Anthropic Chat Model: Orchestrates tool calls, pulls in memory, and crafts a clear, user-friendly answer.
• Google Sheets: Captures queries, answers, timestamps, and evidence so you can monitor and iterate.

Once this is in place, each new request flows through the system with minimal friction, turning a complex research task into a smooth, repeatable operation.

Step-by-step: build your n8n Visa Requirement Checker

Let’s walk through the workflow so you can recreate and then extend it. Think of each step as another layer of automation you are adding to your toolkit.

1. Create the webhook endpoint that starts everything

Begin in n8n with a Webhook node:

• Method: POST
• Path: for example /visa_requirement_checker

Expect a JSON body like:

{  "passport_country": "India",  "destination_country": "Germany",  "purpose": "tourism",  "travel_dates": "2025-05-10 to 2025-05-20"
}

Validate and sanitize this data. These fields will drive both the search query and the log entry in Google Sheets later. Once this is in place, you already have a reusable API endpoint that any app or form can call.

2. Prepare your knowledge base with a Text Splitter

Your system is only as good as the documents behind it. Take your policy documents, embassy pages, or other official sources and feed them into an n8n Text Splitter node.

Recommended configuration:

• chunkSize: 400
• chunkOverlap: 40

This gives a strong balance between preserving context and keeping vector length efficient. Over time, you can adjust these values based on your documents and accuracy needs.

3. Turn text into vectors with Cohere embeddings

Next, connect your chunks to a Cohere Embeddings node.

• Select the embedding model that fits your subscription and language requirements.
• For each chunk, store metadata such as:
  • Source URL
  • Document title
  • Scrape or publication date
  • A unique document ID

This metadata becomes invaluable later for traceability, audits, and user trust. You are not just answering questions, you are building an explainable knowledge layer.

4. Insert your vectors into Weaviate

With embeddings in hand, use the Weaviate node in insert mode.

• Create or reuse a class/index, for example visa_requirement_checker.
• Insert the vector along with all associated metadata and IDs.

By including unique IDs, you make it easy to update or delete records later instead of duplicating them. This is what keeps your system maintainable as visa rules evolve.

5. Enable real-time search with a Weaviate query node

Once the data is inserted, you are ready to answer real questions. At runtime, add a Weaviate Query node to your workflow.

Use the webhook payload to craft a natural language search query such as:

“visa requirement for an Indian passport holder traveling to Germany for tourism”

Configure the node to:

• Perform a similarity search over your visa_requirement_checker index
• Return the top N chunks, for example between 3 and 10, depending on the breadth of your documents

This step turns raw text into targeted evidence that your agent can reason over.

6. Wrap the query as a tool and add memory

To give your system more intelligence, wrap the Weaviate query as a tool that an n8n Agent can call.

Then add a Memory node, typically a buffer window, so the agent can:

• Remember recent user questions in a session
• Provide consistent follow-ups
• Build context instead of treating each query as isolated

This moves your workflow closer to a conversational assistant that can handle clarifications and repeated checks without losing track.

7. Use an Anthropic chat model through an Agent

Now connect an Anthropic chat model via an n8n Agent node. Configure the Agent to:

• Call the Weaviate tool to fetch the most relevant chunks
• Combine retrieved documents with the current memory context
• Generate a concise, user-friendly answer that:
  • References the underlying sources
  • Outlines next steps or official links where appropriate

This is where your workflow becomes truly transformative. Instead of returning raw data, it returns clear guidance, backed by evidence, that your users can act on immediately.

8. Log everything to Google Sheets for learning and growth

Finally, connect a Google Sheets node to append each interaction to a sheet named, for example, Log.

Capture fields such as:

• Original query payload (passport country, destination, purpose, dates)
• System response
• Timestamp
• Evidence URLs or document IDs used

This log becomes your feedback loop. You can:

• Review responses for quality and accuracy
• Identify missing documents or edge cases
• Spot trends in user questions that inform product decisions

Over time, this turns your visa checker into an evolving, data-driven asset rather than a static tool.

Best practices to keep your system accurate and trustworthy

Designing a smart chunking strategy

Chunking is a small configuration choice that has a big impact:

• Too small and you lose context, which can weaken search results.
• Too large and you dilute relevancy and increase embedding costs.

Start with chunkSize = 400 and chunkOverlap = 40, then test and adjust. Use your Google Sheets logs to see where answers feel too vague or too narrow and refine accordingly.

Metadata and provenance as a trust signal

Always store source metadata with each vector:

• Official URL or source name
• Scraped or publication date
• Document title

When you present answers, you can surface this information or at least keep it accessible internally. This helps users verify information and gives you a clear audit trail if a rule changes.

Keeping your data fresh with scheduled updates

Visa rules change and your system should keep up without constant manual intervention. Use n8n to create a scheduled workflow that:

• Re-scrapes embassy or policy pages on a regular cadence
• Re-ingests updated documents
• Uses your vector IDs to upsert rather than duplicate content

This keeps your checker reliable and significantly reduces the maintenance burden over time.

Safety, disclaimers, and human review

LLMs are powerful, but they can still hallucinate. To stay responsible:

• Add an automatic disclaimer such as:
  “This tool provides guidance based on indexed documents, please confirm with the official embassy or consulate before travel.”
• Consider implementing a confidence threshold where low-confidence answers are flagged for human review.
• Use your Google Sheets log to spot and correct problematic outputs.

This balance of automation and oversight builds trust with users and stakeholders.

Testing, monitoring, and scaling your workflow

Once your visa checker is running, treat it like a living system you can refine.

• Test thoroughly:
  • Unit-test webhook payloads.
  • Run synthetic queries based on known visa rules.
  • Compare agent outputs with a ground-truth dataset where possible.
• Monitor usage and cost:
  • Track Cohere, Anthropic, and Weaviate usage.
  • Implement caching for common queries to reduce repeated calls.
• Scale when traffic grows:
  • Scale Weaviate with additional replicas.
  • Isolate the inference layer to handle higher concurrency.

With these habits in place, your visa checker can grow from a prototype into a dependable service that supports your users at scale.

A quick look at the end-to-end webhook flow

To recap the journey a single request takes:

1. The user sends a POST request with passport, destination, purpose, and dates.
2. The Agent turns that payload into a search query.
3. The Weaviate Query node fetches the top relevant chunks from your vector store.
4. The Anthropic chat model summarizes the evidence into a clear answer with citations or references.
5. Google Sheets logs the entire exchange for analysis and improvement.

What once took minutes of manual research now completes in seconds, consistently and at scale.

Costs and practical considerations

As with any AI-powered workflow, it helps to understand where your main costs come from:

• Embedding generation with Cohere when you ingest or update documents
• Chat completions with Anthropic at runtime for each user query

To keep things efficient:

• Use batching when generating embeddings in bulk.
• At runtime, retrieve only as many chunks as you need for good coverage.
• Favor shorter prompts and responses when they still answer the question clearly.

This lets you scale responsibly without sacrificing user experience.

Turning this template into your automation launchpad

By combining n8n, Cohere, Weaviate, and Anthropic, you are not just building a visa requirement checker. You are building a reusable pattern for AI-powered, explainable automation that can adapt as your needs grow.

A simple path to get started:

1. Begin with a limited dataset, such as the top 50 countries or embassies your users care about most.
2. Validate the workflow’s answers against official sources.
3. Iterate on chunking, prompts, and metadata based on your Google Sheets logs.
4. Gradually expand coverage and add new features as your confidence grows.

Each improvement you make here strengthens your broader automation capabilities and frees more time for deep work and strategic decisions.

Call to action: Ready to build your own Visa Requirement Checker and start automating more of your research work? Export this n8n template, connect your Cohere, Weaviate, and Anthropic credentials, and deploy the webhook.

If you would like, I can help you with a sample n8n export (JSON) tailored to your document sources or with webhook validation code. Just share which countries or data sources you want to support first, and we can shape the workflow around