Most PE teams are experimenting with AI. Few have made the output investment-grade. Here's how we close the gap.
The tools are powerful — the implementation isn't. Adoption remains unstructured, and the output rarely meets the bar your IC expects.
AI is typically implemented by IT teams who've never seen an IC memo. Investment professionals, meanwhile, don't know how to extract real value from these tools. AI creates noise — not an edge.
LCAT bridges the gap between investment teams and AI capabilities. We've sat in the deal room and trained the models — so we understand both sides.
We don't implement tools. We redesign how your team works with AI, using a methodology built from hundreds of hours training AI models for financial analysis.
Write a strong initial request with context, audience, and format requirements.
5–9 follow-up turns to challenge, validate, and sharpen — like briefing a sharp analyst.
Memos, models, and decks at the quality level your partners expect.
Common hallucinations, where the model cuts corners, and the failure patterns specific to financial analysis — so every output is challenged before it reaches the IC.
"AI rarely gets it right on the first try. The skill is knowing how to follow up — what to challenge, what to validate, and when to push harder. That's what we teach."
From a short diagnostic to ongoing advisory — designed to fit how PE teams actually adopt new tools.
We map your current deal workflow and identify where AI creates the highest impact — using your real deal materials.
Structured training for your senior investment team on extracting analyst-quality output from AI tools.
AI models evolve every few months. We stay on as your dedicated AI advisor — keeping your team current and workflows sharp.
We work with a limited number of funds. If you're exploring how AI can improve your deal execution, we're happy to share concrete examples.
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