Harvey, Hebbia, Rogo and Build: The AI Stack Reshaping Professional Services in Real Estate
Four platforms, four distinct specializations, what each one actually does and how to decide which belongs in your stack.
The professional services AI market has clarified. A year ago, teams were experimenting with general-purpose ChatGPT wrappers and calling it an AI strategy. In 2026, the category leaders are defined, Deployments at institutional organizations are producing real ROI and the distinction between platforms is no longer about AI vs. no AI, it's about which platform is purpose-built for your specific workflow.
Four platforms come up most frequently in conversations at institutional real estate developers, private equity firms and the legal and financial advisors they work with: Harvey, Hebbia, Rogo and Build. Each occupies a distinct lane. Each has genuine limitations. Here's what they actually do.
Harvey, Legal AI for the Built World's Law Firms
Harvey is a legal AI platform built specifically for law firms and corporate legal teams. Its core use case: accelerating legal document work, drafting, reviewing and analyzing contracts, memos and legal research.
What it does well:
Harvey has strong performance on legal document drafting and clause-level review. Law firms handling real estate transaction work, purchase agreements, development agreements, loan documents, ground leases, easements, use Harvey to accelerate first-draft production, redline generation and contract comparison.
For real estate developers, Harvey's value comes through their legal counsel. It's not a tool you deploy internally; it's a tool that makes your law firm faster and (in theory) less expensive to engage.
Who it's for: AmLaw 100 and regional law firms with significant real estate transaction volume. Corporate legal teams at large REITs and developers.
Where it excels: Legal document drafting, comparative contract analyzis, legal research memo generation.
Honest limitations: Harvey is a legal tool, not a development workflow tool. It won't help you screen a site, build a pro forma or analyze a market. It also requires your law firm to have adopted it, you can't deploy it on your own for legal work without a law firm context.
Pricing signals: Enterprise contracts, not publicly disclosed. Typically deployed at the firm level, not per-matter.
Hebbia, Deep Document Research and Analyzis
Hebbia is a document research platform designed for knowledge workers who need to synthesize information across large document sets. Its differentiation: multi-document reasoning. Hebbia doesn't just search documents, it reads across a corpus and generates structured answers with source citations.
What it does well:
Institutional investment teams use Hebbia to analyze offering memoranda, financial filings, lease abstracts and due diligence packages. For real estate applications, the most common use cases are: analyzing a lease portfolio for a potential acquisition, reviewing a data room across dozens of documents for a transaction and extracting terms from large document collections.
Hebbia's Matrix feature allows users to define a set of questions and run them across every document in a folder, returning a structured table of answers with citations. For a 200-document data room, this is a meaningful acceleration.
Who it's for: Private equity and investment teams, asset managers, acquisitions teams at institutional developers. Anyone with a high-volume document analyzis burden.
Where it excels: Multi-document synthesis, structured data extraction from unstructured documents, large-corpus research.
Honest limitations: Hebbia is a research and analyzis platform, not a workflow automation platform. It doesn't trigger actions or connect to external data sources dynamically. It's excellent for reading and synthesizing; it's not an autonomous agent.
Pricing signals: Per-user SaaS pricing with enterprise tiers. More accessible than Harvey at the team level.
Rogo, Financial Analyzis AI
Rogo is a financial AI platform focused on accelerating quantitative analyzis for financial services teams. Its core capability: ingesting financial data and generating analyzis, comparisons and modeling support.
What it does well:
Investment banking, private equity and real estate capital markets teams use Rogo to accelerate market sizing, comparable analyzis, financial modeling support and report generation. For real estate, the relevant use cases include cap rate trend analyzis, sector comparables, REIT financial analyzis and capital markets research.
Rogo's strength is in structured financial data processing and synthesis. It can ingest earnings transcripts, financial statements and market data feeds and produce analytical output faster than a junior analyzt working manually.
Who it's for: Financial services teams, investment banks, PE firms, asset managers with active CRE capital markets exposure. Less relevant for development operations teams.
Where it excels: Financial data synthesis, sector analyzis, comparables, capital markets research support.
Honest limitations: Rogo is optimized for financial analyzis, not development operations. It won't help with entitlement research, site selection, construction monitoring or the operational Workflows that define development execution. It's a capital markets tool with real estate applications, not a built-world development tool.
Pricing signals: Enterprise pricing with financial services orientation. Adoption has been concentrated in capital markets and investment banking contexts.
Build, Agentic AI for the Built World
Build is purpose-built for institutional real estate development workflows, specifically the development execution operations that the other platforms don't touch: site selection, entitlement research, market analyzis, pro forma modeling, document review and construction monitoring.
What it does well:
Build deploys agentic AI workflows for development teams. Where other platforms handle specific analyzis tasks, Build automates multi-step development processes, running a site screen, pulling zoning and overlay data, running the entitlement research, initializing the feasibility model. These are sequences of tasks, not single-query lookups.
The forward deployment model means Build's workflows are configured to a specific team's existing data, tools and processes. The output is integrated into the team's workflow, not a separate research tool they need to query.
Who it's for: Institutional real estate developers, CDOs and VP-level development leaders at organizations running active project pipelines across markets.
Where it excels: Development operations automation, multi-step workflow execution, built-world specific domain depth (zoning, entitlement, power infrastructure, pro forma logic).
Honest limitations: Build is specialized for development operations. It's not a capital markets research tool (Rogo handles that better), and it's not a legal document platform (Harvey handles that better). The depth is in the built world; breadth is not the product.
Pricing signals: Enterprise deployment with forward-deployed model, not off-the-shelf SaaS. Contact for deployment scoping.
The Buyer's Decision Framework
Before selecting any of these platforms, a development organization should answer three questions:
1. What workflow are you solving?
Legal document drafting/review → Harvey (via your law firm)
Large-scale document research and due diligence → Hebbia
Financial analyzis and capital markets → Rogo
Development operations and workflow automation → Build
These are not substitutes for each other. A serious organization running multiple high-value workflows will deploy more than one.
2. Where does the bottleneck actually sit?
AI investment that doesn't address the real bottleneck produces no ROI. If your team spends more time on entitlement research than on financial analyzis, deploying Rogo first is the wrong sequence.
3. How does this integrate with your existing stack?
Standalone tools that don't connect to your data produce point-in-time answers, not workflow automation. Evaluate each platform on its integration model, not just its demo output.
What institutional teams are running in 2026:
The most sophisticated developers are deploying purpose-built tools for each workflow layer rather than expecting a single general-purpose AI to handle everything. Build for development operations. Hebbia for due diligence. Legal AI through their counsel. Financial analyzis through their capital markets advisors or Rogo if they have an in-house investment team.
The platforms that will lose are the general-purpose tools that don't have domain depth and the legacy data platforms that bolted AI onto existing products without rethinking the workflow. Domain-specific, workflow-integrated, agentic, those are the characteristics worth evaluating.