When we launched Outcome last October, we made a simple bet. Real estate had spent 25 years buying software that collected, stored, and reported on data, and almost none of it was built to act on that data. We believe firms need a layer that could take their documents, systems, and business logic and turn them into executed work. A year later, that bet has become a year of growth, expansion, and product evolution, from workflow automations with verified extractions and full audit trails to enterprise application rollouts across tens of thousands of properties. In short, we’ve made the AI action layer for real estate a reality.
A year of growth
Since launch, Outcome has grown revenue 18 times and added 13 new customers across office, multifamily, industrial, retail, and student housing. They use Outcome for acquisitions, underwriting, asset management, leasing, capital formation, and building operations. What we find most telling is how different these firms are from one another. They span asset types, firm sizes, and functions, and they came to us for the same reason. They want operating leverage: the ability to improve NOI and grow the business without growing headcount at the same rate.
What our customers built
The work tells the story better than we can.
A global real estate services firm brought Outcome into production to manage building infrastructure risk. The system audits vendor connections, analyzes property onboarding files, and generates executive-ready cybersecurity reviews, replacing manual compilation and vendor communications across IT and operations teams.
A multifamily operator took an underwriting analyst operating system live, pairing document extraction with dynamic pro forma modeling. Underwriting turnaround was cut in half, and math errors on resident rent rolls and operating expenses were eliminated.
An institutional owner processed more than 54,000 documents in a lease-to-rent-roll audit. They checked decades of tenant agreements against active operating records with full auditability, and the work is now expanding into a multiyear application rollout.
An asset manager partnered with us to automate capital formation, connecting LP communications, CRM reconciliation, and deal pipeline tracking in one place.
An industrial investment firm scaled its operating system across 400 assets. It automated property data normalization and lease abstraction so its team can guarantee a 24-hour turnaround on letters of intent.
Every one of these deployments taught us something about what real estate teams need from AI to trust it with real work. Those lessons drove nearly everything we shipped this year.
How the platform evolved
On the platform side, we focused on the problems that make real estate data hard to work with.
Outcome now retains organizational context across sessions, so teams no longer re-upload guidelines or re-explain fund criteria every time they pick up a deal. We introduced an editable verification grid with source citations tied to the exact location in the original document, so reviewers can inspect and correct a value before it moves downstream.
We built entity resolution that matches tenant names, legal entities, and property schedules even when they never line up word for word. We shipped an offering memorandum ingestion engine that reads multi-hundred-page marketing books and maps rent schedules and unit economics into investment-committee-ready models. And we rebuilt how Outcome produces deliverables, so finished work comes out as native artifacts like PowerPoint decks, Word documents, and Excel workbooks formatted exactly the way a firm already works.
We also expanded our library to more than +30 templated outcomes, covering workflows like utility bill audits, LOI generation, lease abstraction, OM abstraction, budget variance reporting, box score summaries, CAM reconciliation, and more. Each template is built around the most common elements of its workflow, which makes it a practical starting point for teams that want to get moving with AI automation. A template is a starting point, though, not a ceiling. The platform is built for flexibility, so customers can start with a template and customize it to their own logic, or build a workflow entirely from scratch. In June, we launched our free trial so any team can see a templated outcome in action before talking to us.
That same month, we launched App Studio, which I’d call the clearest expression of what we mean by “built-to-suit.” App Studio takes a client’s data, workflows, business logic, and validations, and puts an interface on top of all of it. The result is an application where a team has everything it needs and nothing it doesn’t, shaped by how the firm actually operates rather than by how a software vendor assumed it would.
We also made sure customers know exactly what they’re paying for. As AI spend has grown, many firms have been caught off guard by tokenmaxxing and bills that swing with every usage spike. Our enterprise offering is outcome-based instead. Customers pay a fixed price tied to measurable value across staffing, software, and services, so the cost of AI is something they can plan around rather than react to.
Building the team
None of this happens without the people behind it. This year, we added core roles across product, customer success, engineering, and go-to-market. We’re also expanding our team of forward deployed engineers, who work directly with client firms to map their workflows and get Outcome running on their real data. That hands-on work is how we help firms move toward the model we described in Meet the $5B, 5-Person Real Estate Firm of the Future, where technology carries the operational load and people focus on judgment, relationships, and deals.
What comes next
The opportunity in front of us is a $100 billion-plus market, and it’s shaping up to be winner-take-most. As firms move from experimenting with AI to running their operations on it, they will consolidate around the platforms that deliver results they can trust. We intend to be that platform.
In the year ahead, we’re building toward a future where putting AI to work starts with describing what you need. A team will be able to describe a workflow in plain language, and Outcome will build it, test it, and run it end to end, acting on its own as new documents arrive and data changes. The same shift is coming to applications. We’ll keep expanding App Studio’s capabilities so that describing the application your team needs is all it takes for Outcome to build it around your data, workflows, and business logic.
To everyone who believed in Outcome from the beginning, thank you. And to those who’ve joined us along the way, we’re glad you’re here as we bring the real estate AI action layer to more firms.
Prasan Kale, Co-Founder and CEO
Sid Jain, Co-Founder and CTO