How to Integrate Next.js for pension funds with Vercel AI SDK for RAG

Why this integration matters

If you’re building AI for pension operations, the hard part isn’t the chat UI. It’s getting reliable retrieval over policy docs, member statements, contribution rules, and admin workflows without exposing bad answers to users.

Combining Next.js for pension funds with Vercel AI SDK gives you a clean path: Next.js handles the app shell and data access patterns, while Vercel AI SDK powers retrieval-augmented generation in a way that fits agentic workflows. The result is an assistant that can answer fund-admin questions with grounded context instead of guessing.

Prerequisites

• •Python 3.10+
• •A Next.js app already set up for your pension fund portal
• •A Vercel project with AI SDK enabled
• •Access to your pension fund knowledge base:
  • •policy PDFs
  • •contribution rules
  • •retirement benefit documentation
  • •FAQ content
• •API credentials for:
  • •your Next.js backend endpoints
  • •Vercel AI SDK-compatible endpoint or model provider
• •Installed packages:
  • •requests
  • •pydantic
  • •python-dotenv

Integration Steps

1. •

   Expose pension fund data from Next.js

   Your Next.js app should expose a server route that returns the documents your RAG pipeline will index. In practice, this is usually an API route backed by your CMS, database, or document store.

   import requests
   from pydantic import BaseModel
   from typing import List
   
   class PensionDoc(BaseModel):
       id: str
       title: str
       content: str
       source_url: str
   
   NEXTJS_DOCS_URL = "https://your-nextjs-app.com/api/pension-docs"
   
   def fetch_pension_docs() -> List[PensionDoc]:
       resp = requests.get(NEXTJS_DOCS_URL, timeout=30)
       resp.raise_for_status()
       return [PensionDoc(**doc) for doc in resp.json()]
   

2. •

   Chunk and normalize the content for retrieval

   Don’t send raw PDFs or long pages straight into your retriever. Split them into small passages and attach metadata like document ID and source URL so answers can be traced back.

   import re
   from typing import Dict, List
   
   def chunk_text(text: str, chunk_size: int = 900) -> List[str]:
       text = re.sub(r"\s+", " ", text).strip()
       return [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
   
   def build_chunks(docs: List[PensionDoc]) -> List[Dict]:
       chunks = []
       for doc in docs:
           for idx, chunk in enumerate(chunk_text(doc.content)):
               chunks.append({
                   "id": f"{doc.id}:{idx}",
                   "text": chunk,
                   "metadata": {
                       "doc_id": doc.id,
                       "title": doc.title,
                       "source_url": doc.source_url,
                   }
               })
       return chunks
   

3. •

   Index chunks into your retrieval layer used by Vercel AI SDK

   Vercel AI SDK works best when your retrieval layer returns relevant context fast. If you’re using a vector store behind the scenes, push the chunks there first, then query them during generation.

   import os
   import requests
   
   VECTOR_INDEX_URL = os.getenv("VECTOR_INDEX_URL")
   VECTOR_API_KEY = os.getenv("VECTOR_API_KEY")
   
   def upsert_chunks(chunks):
       headers = {
           "Authorization": f"Bearer {VECTOR_API_KEY}",
           "Content-Type": "application/json",
       }
       payload = {"items": chunks}
       resp = requests.post(f"{VECTOR_INDEX_URL}/upsert", json=payload, headers=headers, timeout=60)
       resp.raise_for_status()
       return resp.json()
   
   if __name__ == "__main__":
       docs = fetch_pension_docs()
       chunks = build_chunks(docs)
       result = upsert_chunks(chunks)
       print(result)
   

4. •

   Call the Vercel AI SDK-backed RAG endpoint from Python

   In a production setup, your Next.js app usually hosts the /api/chat route powered by Vercel AI SDK’s streamText or generateText. Your Python service can call that route and pass the user question plus any tenant context.

   import os
   import requests
   
   CHAT_API_URL = os.getenv("CHAT_API_URL", "https://your-nextjs-app.com/api/chat")
   
   def ask_rag(question: str, member_id: str) -> dict:
       payload = {
           "messages": [
               {"role": "system", "content": "You are a pension operations assistant. Use retrieved context only."},
               {"role": "user", "content": question}
           ],
           "context": {
               "member_id": member_id,
               "domain": "pension-funds"
           }
       }
   
       resp = requests.post(CHAT_API_URL, json=payload, timeout=60)
       resp.raise_for_status()
       return resp.json()
   
   answer = ask_rag(
       "What is the vesting period for employer contributions?",
       member_id="M-10293"
   )
   print(answer["text"])
   

5. •

   Wire response citations back into Next.js

   For pension use cases, citations are not optional. Return source references from your RAG response and render them in the Next.js UI so admins can verify what the model used.

   from typing import Any
   
    def format_answer(response: dict[str, Any]) -> dict:
        return {
            "answer": response.get("text", ""),
            "citations": response.get("citations", []),
            "confidence": response.get("confidence", 0.0),
        }
   
    # Example post-processing before sending to frontend
    formatted = format_answer(answer)
    print(formatted)
   

Testing the Integration

Use a known pension policy question and confirm that the answer includes grounded citations from your indexed documents.

def test_integration():
    result = ask_rag(
        "Can a member take early retirement before age 55?",
        member_id="M-10293"
    )

    assert "answer" in result or "text" in result
    print("Answer:", result.get("text"))
    print("Citations:", result.get("citations", []))

if __name__ == "__main__":
    test_integration()

Expected output:

Answer: Early retirement may be allowed under specific plan rules...
Citations: [
  {"title": "Early Retirement Policy", "source_url": "..."},
  {"title": "Member Benefit Rules", "source_url": "..."}
]

Real-World Use Cases

• •

  Member support assistant

  • •Answer questions about contributions, vesting, withdrawal rules, and retirement eligibility using indexed fund documents.
• •

  Admin ops copilot

  • •Help staff find policy references faster when handling escalations or compliance checks.
• •

  Document-grounded workflow agent

  • •Route member queries into Next.js workflows while using Vercel AI SDK for retrieval and response generation with citations.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.