The Problem
On Monday you tested the 3 prompts in ChatGPT. Cool! You saw how extraction → validation → questions works for investment analysis. But here's the thing: you can't ask your analysts to copy-paste 200 times per day. One analyst spending 3 hours running prompts manually? That's $90/day in labor costs. Multiply that across a busy investment firm and you're looking at $27,000/year just on deal screening. Plus the copy-paste errors that lead to missed opportunities or bad investments.
See It Work
Watch the 3 prompts chain together automatically. This is what you'll build.
Watch It Work
See the AI automation in action
The Code
Three levels: start simple, add reliability, then scale to production. Pick where you are.
When to Level Up
Simple API Calls
- Sequential processing
- Basic error handling
- Manual review of all results
- Local logging
- Cost: ~$50-100/month in API calls
Add Reliability Layer
- Retry logic with exponential backoff
- Queue system (Redis/RabbitMQ)
- Automated confidence scoring
- Structured logging (Winston/ELK)
- Database for results tracking
- Cost: ~$300-500/month (API + infrastructure)
Framework Orchestration
- Multi-step workflow orchestration
- Parallel processing where possible
- Smart caching of common queries
- Real-time monitoring dashboard
- A/B testing of prompts
- Integration with deal management systems
- Cost: ~$800-1,500/month
Multi-Agent System
- Dedicated agents per task type
- Load balancing across API providers
- Advanced caching and rate limiting
- Custom model fine-tuning
- Real-time market data integration
- Automated deal scoring and ranking
- Multi-region deployment
- Cost: Custom pricing, typically $2,000-5,000/month
Real Estate Tech Gotchas
Industry-specific challenges you'll hit. Here's how to handle them.
Market Data Staleness
Implement time-based cache invalidation + real-time API checks for high-value deals.
# Cache with TTL import redis import json from datetime import timedelta redis_client = redis.Redis(host='localhost', port=6379, decode_responses=True) def get_market_data(address: str, max_age_hours: int = 24):
Multi-Currency Handling
Detect currency from address, convert to USD for comparison, store both values.
# Currency handling
import requests
from typing import Dict
def normalize_price(price: float, currency: str, address: str) -> Dict:
# Detect currency if not provided
if not currency:
currency = detect_currency_from_address(address)Incomplete Listing Data
Use ML to estimate missing values based on similar properties + neighborhood averages.
# Estimate missing values
def estimate_missing_data(deal: Dict, comps: List[Dict]) -> Dict:
# Calculate neighborhood averages
avg_hoa = sum(c.get('hoa', 0) for c in comps) / len(comps)
avg_tax_rate = sum(c.get('tax_rate', 0) for c in comps) / len(comps)
# Estimate rent using price-to-rent ratio
avg_ptr = 0.008 # typical 0.8% monthly rentRegulatory Variations
Maintain jurisdiction database + flag deals requiring manual legal review.
# Jurisdiction rules
JURISDICTION_RULES = {
'CA': {
'rent_control': True,
'max_annual_increase': 0.05,
'requires_legal_review': True
},
'TX': {Deal Volume Spikes
Auto-scaling queue workers + priority scoring to process best deals first.
# Priority queue with auto-scaling
from celery import Celery
import boto3
app = Celery('investment_analysis')
@app.task(priority=1) # high priority
def process_high_value_deal(deal_id: str):Adjust Your Numbers
❌ Manual Process
✅ AI-Automated
You Save
2026 Randeep Bhatia. All Rights Reserved.
No part of this content may be reproduced, distributed, or transmitted in any form without prior written permission.