Leni

AI infrastructure that enables 100% accuracy at 1/10th of the cost for enterprise back office.

News:

[Leni @lenianalyst]

We hit one million! 🎉

[Arunabh Dastidar @arunabh_D]

"Mostly right" is useless when millions of dollars are on the line.

That's why we built Leni: the world's most accurate AI for investment work. It analyzes hundreds of files at once, reasons through complex problems, validates its outputs, and delivers finished work instead of making users babysit prompts.

Over the last few years, Leni has been used by institutions to manage over $80B in assets, and we have learned that the biggest challenge with AI isn't intelligence.

It's trust.

Many AI systems sound convincing. Few can be trusted for serious investment decisions.

So we aimed to build one that is accurate, verifiable, and dependable.

Everything we've learned from processing over 100 million rows of data, building our own verification models, evaluation frameworks, and investment workflows, is now embedded into a powerful self-serve platform designed to help investors make institutional-grade decisions with confidence.

Built for serious work.

Built for accuracy.

Try Leni today and experience the difference. 🚀

[Madni Aghadi @hey_madni]

"just answer yes or no"

[Leni @lenianalyst]

Shoutout to my friend Marcio and his AI in Real Estate channel. 💛

He automated an interactive scorecard, showing a Real estate portfolio management platform in action. 📊

Watch the walkthrough and tell him Leni sent you.

#RealEstateAI #CRE #AI

[Leni @lenianalyst]

How long did your last pricing report take to put together?

July 15, we're joining AI for CRE Collective to build a custom multifamily AI analyst, live. With Elaine Williams and Marcio Sahade.

Surprise for everyone who joins. 👀

1pm PT | 4pm ET

Link in bio 🔗

[Leni @lenianalyst]

Boldly reconciling where no spreadsheet has gone before.

#FinanceMemes #AssetManagerLife #FinanceHumor #TrekkieFinance #PortfolioProblems

[Leni @lenianalyst]

Clowned by my own pro forma.

#CRE #RealEstateFinance #PrivateEquity #AssetManagement #FinanceMemes

[Yasmine Khosrowshahi @yasminekho]

Stanford professor Judy Fan went on stage at MIT and broke down why humans are so good at making the invisible visible...

And why AI hasn't actually learned to "see" the way we do.

It completely changes how you think about Human Intelligence v/s Artificial Intelligence:

1. Nature never gave us straight lines or sharp corners. The number line, the coordinate plane, even basic geometry are all human inventions. We created tools that do not exist in nature simply because we needed a way to think more clearly.

2. The coordinate system Descartes invented solved a problem that had stumped mathematicians for centuries, doubling the volume of a cube. Once invented, this tool became so indispensable that virtually every math curriculum on Earth still depends on it.

3. Humans have been doing this for at least 30,000 to 80,000 years. The story of human progress is inseparable from the story of marking up our environment, from cave walls to Galileo's telescope to Feynman diagrams of particles we will never see with our own eyes.

4. Every major scientific breakthrough relied on a visual tool that made something invisible visible. Darwin needed side-by-side illustrations of finches to see variation that was otherwise too subtle to notice. Cajal needed detailed drawings of neurons under a microscope to map how the nervous system was wired.

5. Fan's research group studies something deceptively simple: how people decide what to put into a drawing and what to leave out. When two people played a drawing game, sketchers used far more detail when the target object had close competitors than when it stood alone, all the way down to using fewer strokes and less time when more detail was not necessary.

6. People are not just copying what they see. They are making constant judgment calls about what level of detail actually serves the goal of communication, and they do this naturally without ever being taught the theory behind it.

7. There is a real difference between drawing something so someone can identify it and drawing something so someone can understand how it works. In one study, participants drew explanatory diagrams that emphasized moving, causal parts of a machine while depictive drawings emphasized background and overall appearance, even though both were drawing the exact same object.

8. Explanatory drawings were genuinely better at helping someone figure out how to operate a machine, but worse at helping someone identify which machine it actually was. You cannot optimize a single drawing for both goals at once. Communication always involves tradeoffs.

9. AI vision models trained on photographs generalize surprisingly well to simple, sparse sketches, suggesting that resemblance based recognition is not just a story we tell ourselves. It is something modern neural networks can replicate with real accuracy.

10. But there remains a large, measurable gap between how confidently AI models recognize sketches and how confidently humans do, even when both groups answer the same questions about the same images. Humans are simply far more reliable and far more consistent in their judgments.

11. When researchers compared human-made sketches to AI-generated sketches under tight stroke budgets, both were similarly recognizable at higher budgets, but diverged sharply as the budget shrank. Humans and AI systems simplify drawings in fundamentally different ways once resources get scarce.

12. Reading a graph is not one single skill. It involves perception, knowing where to look, mapping that visual information onto the actual question being asked, and then translating that mapping into an answer. Each of these steps can independently break down, and people fail for very different underlying reasons even when they land on the same wrong answer.

13. When tested directly against humans on graph reading tasks, leading multimodal AI models, including GPT-4V, showed a meaningful performance gap. Even when a model's overall accuracy approached human levels, its pattern of mistakes looked nothing like how humans actually get things wrong.

14. People choose entirely different types of charts depending on what specific question they are trying to answer, not out of a generic preference for bar charts or scatter plots. Their chart choices closely tracked which visualization would genuinely help someone answer that specific question correctly.

15. Two of the most widely used graph literacy tests in education research turned out to correlate strongly with each other, suggesting they measure overlapping skills. But when researchers dug into the actual error patterns, the standard categories used in textbooks, like "find the maximum" or "identify a cluster," failed to explain why people got things wrong nearly as well as a more basic, underlying four-factor model did.

16. The deepest goal behind all of this research is not just academic curiosity. It is to eventually help students and everyday people develop genuine literacy with the visual tools that science and modern decision-making increasingly depend on, because every generation should be able to see further than the last by standing on the visual tools the previous generation built.

Follow @yasminekho for more ideas on thinking better, becoming clearer & building a more intentional life.

[Leni @lenianalyst]

This industry is moving. And I'm paying attention.
Thank you to NAIOP GTA and Osler for the conversation. 🤝

[Leni @lenianalyst]

Marcio was at NAIOP GTA today with Jennifer Allard (HOOPP) and Nishat Jessa (Oxford Properties) talking AI in Canadian real estate. 👀
The room raised their hands at Stage 3.

[Leni @lenianalyst]

Faster deal analysis. Underwriting that doesn't wait. Month-end reporting done right the first time.
That's what Stage 3 looks like on the ground. 📁

[Leni @lenianalyst]

Reporting culture in one meme. 📊

#FinanceTwitter #MoneyTalks

[Leni @lenianalyst]

goals only some of us can understand ⚽

#financehumor #fifaworldcup #AIforInvestors #analystlife #investmentbanking

[Leni @lenianalyst]

CRE firms keep asking how much time AI can save.
But the bigger question is how much value it is actually creating. 💰

Deloitte found that the share of CRE executives reporting transformative impact from AI fell from 12% to 1% in a single year. Even as adoption kept rising.

A new model won't flag scattered data, inconsistent definitions, and workflows held together by smart people and complicated spreadsheets. It will just move through them faster.

@arunabh_D wrote about where the return actually comes from. Link in bio 👆

[Arunabh Dastidar @arunabh_D]

Had a very cool day at @Google HQ with Google and DeepMind folks. Love the deep alignment with teams like us, building specialized ecosystems and models for low-latency, high-accuracy outputs. Cheers @GoogleStartups!

[Leni @lenianalyst]

See the VAR. Feel the VAR. Become the VAR.

#WorkMeme #OfficeHumor #TechAtWork #VAR #FIFAMemes

[Arunabh Dastidar @arunabh_D]

1/ Last week, Fable 5 came out. A 6-trillion-parameter model. And, like clockwork, everyone lost their minds. Some people were paid to rave about it. Others did it for free, which is honestly even funnier.

[Leni @lenianalyst]

Prepared, not lucky.
That's how I like it.

#investing #markets #portfolio #riskmanagement #financememes

[Leni @lenianalyst]

@mr_farman_ai Thanks! checking it out

[Leni @lenianalyst]

1M views. 🤖

The team wanted to say thank you.

So, thank you. 💛

Still a lot to learn, improve, and prove.

More soon. 📊

[Leni @lenianalyst]

@Soaib_TechAi @TheSomitraSR thank you!

[Leni @lenianalyst]

@aaliya_va thank you for your support and feedback! 💛