How to Integrate LlamaIndex for lending with Supabase for startups

Combining LlamaIndex for lending with Supabase gives you a practical stack for building loan-focused AI agents that can retrieve borrower data, summarize documents, and write decisions back to your app database. For startups, this is useful when you need a system that can answer lending questions from policy docs, customer records, and application history without building a full data platform first.

Prerequisites

• •Python 3.10+
• •A Supabase project with:
  • •SUPABASE_URL
  • •SUPABASE_SERVICE_ROLE_KEY
• •A PostgreSQL table for lending records or documents
• •LlamaIndex installed with the relevant integrations
• •An embedding model provider configured, such as OpenAI
• •Basic knowledge of SQL and Python async/sync calls
• •Environment variables stored in .env

Install the packages:

pip install supabase llama-index python-dotenv openai

Integration Steps

1. •Set up your environment variables.

Use .env so your app can connect to Supabase and LlamaIndex-backed LLM components without hardcoding secrets.

from dotenv import load_dotenv
import os

load_dotenv()

SUPABASE_URL = os.getenv("SUPABASE_URL")
SUPABASE_SERVICE_ROLE_KEY = os.getenv("SUPABASE_SERVICE_ROLE_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

2. •Connect to Supabase and fetch lending records.

This example reads loan applications from a loan_applications table. In a real startup workflow, this table usually stores borrower profile fields, requested amount, status, and document URLs.

from supabase import create_client, Client

supabase: Client = create_client(SUPABASE_URL, SUPABASE_SERVICE_ROLE_KEY)

response = (
    supabase.table("loan_applications")
    .select("id, applicant_name, requested_amount, income, status")
    .eq("status", "pending")
    .execute()
)

applications = response.data
print(applications)

3. •Convert Supabase rows into LlamaIndex documents.

LlamaIndex works best when your structured records become retrievable text chunks. For lending use cases, include the fields an agent needs to reason over: income, debt ratio, employment type, and application status.

from llama_index.core import Document

documents = []
for row in applications:
    text = (
        f"Application ID: {row['id']}\n"
        f"Applicant Name: {row['applicant_name']}\n"
        f"Requested Amount: {row['requested_amount']}\n"
        f"Income: {row['income']}\n"
        f"Status: {row['status']}"
    )
    documents.append(Document(text=text, metadata={"application_id": row["id"]}))

4. •Build a LlamaIndex query engine over the lending data.

This creates an in-memory index for startup prototypes. If you want persistence later, swap in a vector store backed by Postgres or another database.

from llama_index.core import VectorStoreIndex

index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()

result = query_engine.query(
    "Which pending applications have requested amounts above 50000?"
)

print(result)

5. •Write the agent result back to Supabase.

A common pattern is to store the AI output in an underwriting_notes column or a separate loan_decisions table. That keeps your agent auditable.

decision_payload = {
    "application_id": applications[0]["id"],
    "decision_note": str(result),
}

upsert_response = (
    supabase.table("loan_decisions")
    .upsert(decision_payload)
    .execute()
)

print(upsert_response.data)

Testing the Integration

Run a simple end-to-end check: fetch one record from Supabase, index it with LlamaIndex, query it, then persist the response back into Supabase.

test_query = query_engine.query("Summarize the first pending loan application.")
print("LLM RESULT:", test_query)

verify = (
    supabase.table("loan_decisions")
    .select("*")
    .eq("application_id", applications[0]["id"])
    .execute()
)

print("SAVED ROW:", verify.data)

Expected output:

LLM RESULT: Application ID 123 is pending with requested amount 75000 and income 120000.
SAVED ROW: [{'application_id': 123, 'decision_note': '...'}]

Real-World Use Cases

• •

  Loan application triage

  • •Pull pending applications from Supabase.
  • •Use LlamaIndex to summarize risk signals from borrower notes and uploaded docs.
  • •Store review notes or recommendation scores back in Supabase.
• •

  Policy Q&A for underwriters

  • •Index internal lending policy documents.
  • •Let agents answer questions like “What debt-to-income ratio is acceptable for SMB loans?”
  • •Keep policy citations alongside answers for auditability.
• •

  Customer support for lending ops

  • •Retrieve application status from Supabase.
  • •Use LlamaIndex to generate plain-English explanations of missing documents or approval delays.
  • •Log every interaction for compliance review.

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.