The Problem
On Monday you tested the 3 prompts in ChatGPT. You saw how extraction → validation → matching works for finding strategic partners. But here's the reality: you can't ask your BD team 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 growing company and you're looking at $27,000/year just on partnership admin. Plus the inconsistency that leads to missed opportunities and misaligned partnerships.
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 Scale
Simple API Calls
- Sequential prompt chaining
- Basic JSON parsing
- Simple retry logic
- Manual monitoring
Add Reliability
- Exponential backoff retries
- Structured logging (Winston/Python logging)
- Timeout handling
- Basic metrics tracking
- Queue for batch processing
Framework with Orchestration
- State machine workflow (LangGraph)
- Parallel processing
- Advanced error recovery
- Database integration
- Analytics dashboard
- A/B testing prompts
Multi-Agent System
- Specialized agent roles
- Load balancing
- Real-time monitoring
- Auto-scaling infrastructure
- Advanced caching
- Custom model fine-tuning
- Multi-region deployment
Strategy-Specific Gotchas
Real challenges you'll face automating partnership analysis. Here's how to handle them.
Inconsistent Company Data Formats
Use structured prompts with examples and validation
# Robust extraction with format normalization
def normalize_company_size(raw_text: str) -> Dict:
"""Handle various company size formats"""
extraction_prompt = f"""Extract company size and normalize to standard format.
Handle formats like:
- "150 employees, $25M ARR"
- "mid-market SaaS company"Strategic Fit is Subjective
Build custom validation rules for your specific partnership criteria
// Custom validation rules based on partnership type
interface ValidationRules {
partnershipType: 'technology' | 'channel' | 'strategic';
requiredFields: string[];
criticalThresholds: Record<string, number>;
}
function validateByPartnershipType(Partnership History Context is Critical
Build a partnership knowledge base and inject relevant context
# Partnership knowledge base integration
from typing import List
import chromadb
class PartnershipKnowledge:
def __init__(self):
self.client = chromadb.Client()
self.collection = self.client.create_collection("partnerships")Real-Time Data Requirements
Integrate with live data sources and flag staleness
// Real-time data enrichment
import axios from 'axios';
interface EnrichmentSource {
name: string;
api: string;
fields: string[];
}Multi-Stakeholder Alignment
Multi-dimensional scoring with stakeholder weights
# Multi-stakeholder partnership scoring
from typing import Dict, List
import numpy as np
class PartnershipScorer:
def __init__(self):
# Define stakeholder priorities (weights sum to 1.0)
self.stakeholder_weights = {Adjust Your Numbers
❌ Manual Process
✅ AI-Automated
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