We raised $8.5 million to help the investment world adopt AI.
Everyone talks about models. Almost nobody talks about the architecture required to make them work in the investment world. That is where adoption breaks. Without a purpose-built foundation, firms are left building everything themselves: data architecture, workflows, permissions, security, context, and system logic before AI can produce anything trustworthy.
Meanwhile, the demand is already here. In wealth and asset management, EY found that 95% of firms have already scaled generative AI to multiple use cases. McKinsey estimates agentic AI could generate roughly $430 billion to $550 billion annually across real estate, construction, and development globally. But that value will not come from experimentation alone. McKinsey is clear that it depends on structured data, integration into core systems, workflow redesign, and clear controls.
Investment teams should not be in the business of assembling AI infrastructure. They should be in the business of making decisions, moving capital, and generating returns.
Leni is built so they can.
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In the News
Leni in the News
A few recent highlights and third‑party write-ups featuring Leni from major product launches to ecosystem partnerships.
Sahade reflects on new role at Leni, AI's use in commercial real estate
Canada’s Top Companies to Watch in 2025 🇨🇦
Leni Launches Industry-First Universal Data Model for Multifamily Real Estate
The Leni Story.
Leni began with a simple frustration: real estate and investment teams were making high-stakes decisions with information scattered across PDFs, spreadsheets, emails, and a dozen disconnected systems.
We started by tackling the hardest problem first: data. Leni’s Universal Data Model brought structure to asset-class workflows, and we became the first to launch it across all seven major ERPs in the sector. From there, Leni evolved into an agentic AI architecture purpose-built for such complex workflows.
Leni has become the go-to architecture for software and business teams across investment and real estate to adopt AI without becoming AI experts or building infrastructure from scratch. It is designed to handle security, avoid dependence on any single model, and reduce the babysitting that generic AI tools often require. Instead, Leni works as a model-agnostic, multi-agent system built to complete specific workflows end to end. You can hand Leni the materials and the goal, step away, and come back to a real deliverable.
The goal from day one was to pair deep vertical knowledge with infrastructure that teams can customize to match how they operate.
Today, Leni (Leading Engine for NextGen Intelligence) is an intelligence layer for owners, operators, and investment teams who want to move faster, stay rigorous, and spend less time assembling information and more time making decisions.
Leni's Leadership Series
From market research to recurring portfolio reporting, hear how leaders are integrating AI in their businesses.
Engage Your Audience with Compelling Content
Leni at Developer's Den
A top-tier development case competition done with Leni.
Inspiring the Future
Leni's campus program inspires the next generation of leaders to be AI-ready.
Shaping Canada's Talent Future
On Stage - What it Takes to Attract Great Talent with the City of Toronto.
Sharing What We Learned
Shared panel with industry veterans sharing stories from actual AI adoption cases.
Leni at Developer's Den
A top-tier development case competition done with Leni.
Inspiring the Future
Leni's campus program inspires the next generation of leaders to be AI-ready.
Shaping Canada's Talent Future
On Stage - What it Takes to Attract Great Talent with the City of Toronto.
Sharing What We Learned
Shared panel with industry veterans sharing stories from actual AI adoption cases.
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Pushing AI Forward
Leni is proud to be a Fast Lane member of the Vector Institute one of the world’s leading AI research hubs, co-founded and advised by Geoffrey Hinton, Nobel laureate and a pioneer of deep learning.
This partnership gives us access to top-tier researchers, Master’s and PhD talent, and applied AI programs that accelerate how we develop, test, and scale advanced models from generative AI to anomaly detection.
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