Efficient implementation path for batch due diligence
VC firms needing to evaluate dozens of startups at once can create a standardized evaluation pipeline with Mira:
- Customized scoring system: Set multidimensional criteria in companyCriteria (e.g., "team technical background strength ≥ 4 points + ARR growth rate > 30%").
- batch mode: Use Promise.all to encapsulate multiple researchCompany calls, 50 companies can complete the initial screening in 2 hours!
- Visualization Comparison: Generate an Excel matrix utilizing the export function of the sample front-end interface, with key metrics (funding history, competitive comparisons) displayed side-by-side
Typical Workflow::
1. Create a list of company URLs (Crunchbase exported CSV recommended)
2. Execute the following script fragment:const results = await Promise.all(
companyUrls.map(url =>
researchCompany(url, config, {companyCriteria: vcStandards})
)
)
3. Sorted by matchScore field, >80 marks prioritized for follow-up
special attention: High-frequency access to Collage may trigger anti-climbing, it is recommended to set it in config{ scrapingBee: {throttle: 2000} } Control request interval
This answer comes from the articleMira: an AI library of intelligences that automate company research and analysisThe
May not be reproduced without permission:AI productivity tools " How can you quickly assess the fit of multiple companies in an investment due diligence scenario?
