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Hebbia Alternatives for CRE Investment Teams

Last reviewed June 2026

Hebbia put a research grid on much of institutional finance. A CRE deal asks for more than reading. Someone still builds the model, drafts the memo in the firm’s voice, trues up the closing, and tracks the hold against the underwrite. This page compares five alternatives by the job each one is actually built to do, and by whether each answers questions about the documents or works the deal itself.

Cap Orbit

that’s us

The AI terminal for institutional CRE with the run of the deal file: it reads every document on the deal at once and carries the deal from first look to a locked closing record and through the hold, in the firm’s own files and formats.

Best for: Acquisitions, credit, and asset-management teams that want the underwrite, the memo, and the record produced in one place, with the analyst accepting every number.

Strengths

One instruction runs a multi-step job end to end: it reads across every file on the deal at once, the offering memo, the rent roll buried in a workbook tab, the T-12, the loan agreement, then normalizes the statements, builds the model, and stages the memo, with the analyst accepting each consequential step.
The model is a real workbook: live formulas, no hardcodes, Base, Upside, and Downside re-priced off one switch, a full return stack and a sensitivity grid, recalculated and checked before delivery, built fresh to an institutional standard or into the firm’s own template in place.
Every figure can be audited: rent rolls and T-12s come out traced to the exact file, sheet, and row or page, footed to the document’s own stated totals, with inferred values marked inferred.
Coverage runs the whole hold: a screening pass the day the materials arrive, IC and credit memos in the house voice that read the model and never write it, settlement reconciliation at closing, append-only period tracking through asset management, and a permissioned exposure read across the book.

Trade-offs

Its data source is the deal folder, not a feed: drop any document in any format onto the deal, broker materials, lender PDFs, scanned pages, spreadsheets, and it reads them. There is no third-party market data, comp, or research subscription; corpus-scale street research stays with a research platform.
Not a pipeline system of record: no contact management, no broker relationship tracking, no deal-flow funnel, and no investor portal or external sharing surface.
Two tiers rather than a self-serve sign-up: Pro puts funds of up to 50 people on live deals within 24 hours; Enterprise deploys into the firm’s own cloud account, with single sign-on and customer-held keys. The evaluation starts with a working session on one live deal.

Rogo

The banker’s platform: multi-step deal work for investment banking and advisory teams, grounded in premium financial data and the systems banks already run.

Best for: Investment banks, advisory firms, and PE deal teams whose work runs on CIMs, comps, and buyer lists rather than rent rolls.

Strengths

Its flagship Felix carries a deal task end to end: ingest a CIM, run the comp analysis, draft the buyer list, and queue the outreach, without anyone re-keying between steps.
Premium data behind the answers: FactSet, S&P Capital IQ, PitchBook, LSEG, and Preqin, alongside the Salesforce, SharePoint, Outlook, and Excel the team already runs.
Model automation through its Subset acquisition: builds Excel models, rolls them forward, refreshes drivers and charts, and adapts to firm templates.

Trade-offs

Nothing property-level: its materials make no reference to rent roll ingestion, T-12 parsing, or DSCR, cap rate, and property cash-flow underwriting of any kind.
The lifecycle it covers is corporate M&A from sourcing to close: no settlement reconciliation, no budget-versus-underwrite tracking through the hold, no portfolio reporting against the original basis.
Enterprise-only and heavy: no public pricing, contracts reported to reach seven figures a year, and implementations that typically run 4 to 12 weeks with the vendor’s own team embedded.

AlphaSense

The research corpus: market intelligence, broker research, and expert calls, sold as a per-seat research subscription.

Best for: Teams whose binding constraint is market intelligence: what the street, the experts, and the filings say, before any deal document arrives.

Strengths

Over 500 million documents in one place: filings, earnings calls, broker research from 1,700 plus providers, news, and trade journals, searched and synthesized with sentence-level citations.
The expert layer: more than 240,000 investor-led call transcripts through the Tegus library, plus live expert calls scheduled from inside the platform.
A maturing analysis layer: standardized financials on 22,000 plus companies, 4,100 plus pre-built Canalyst models, Excel modeling from its Carousel acquisition, and SuperAnalyst, an always-on research assistant launched in June 2026.

Trade-offs

No property documents: rent rolls, T-12s, lease abstracts, and operating budgets sit outside its content universe, and its Excel modeling is built around public-company models, not property cash flows.
Real estate is not one of its industries: the platform lists six verticals and CRE is not among them, and nothing in its materials describes a deal record from screening to closing.
A shared platform sold per seat: private cloud is an enterprise add-on, third-party procurement data puts the median contract near $18,000 a year, and large enterprise contracts run well past $100,000.

BlueFlame AI

The private-markets generalist: knowledge synthesis and memo drafting for dealmakers, with Datasite’s data-room reach behind it.

Best for: Private-markets firms, real estate included, that want drafting and synthesis across sourcing, diligence, and LP reporting more than they want deal math.

Strengths

Names private-markets buyers explicitly: PE, private credit, investment banks, real estate firms, endowments, and hedge funds, with use cases from deal sourcing through IC memo drafting and LP reporting.
Lets the firm route work across Claude, ChatGPT, Gemini, and Grok rather than committing to a single AI provider, and connects to DealCloud, Salesforce, and Microsoft 365.
Operates as a business unit of Datasite, with data-room distribution behind it, SOC 2 Type II certification, and a stated policy that no customer’s data is shared with another.

Trade-offs

It does not build financial models or run structured underwriting: nothing in its public materials describes rent roll or T-12 ingestion, or proforma construction.
It competes on knowledge synthesis and drafting rather than deal math, so the workbook an IC reviews still gets built somewhere else.
Real estate is one audience among several: its public materials describe no CRE-specific lifecycle, no closing reconciliation, and no hold-period tracking.

V7 Go

The extraction specialist: reads the deal documents and populates the proforma, with human review gates on the way through.

Best for: Teams that keep their own models and want the document-to-spreadsheet step done faster, with a person approving each pass.

Strengths

Genuinely CRE-aware extraction: its materials describe reading offering memorandums up to 200 pages and pulling NOI, cap rates, rent rolls, and lease expirations into Excel or ARGUS proformas.
Reaches past the offering memo: its property underwriting offering reads appraisals, environmental reports, and title commitments.
Claims 95 to 99 percent extraction accuracy with human review gates, so a person signs off before figures land in the model.

Trade-offs

It populates models the firm already keeps; it does not build or run the workbook itself.
Extraction is where it stops: no memo drafting in a house voice, no closing reconciliation, and no asset-management tracking appear in its public materials.
A smaller, earlier company than the others here, with $36 million raised, so most of what is public about it comes from its own materials.

The incumbent

Why teams look past Hebbia.

Hebbia holds its seat on real work. Matrix puts a grid over enormous document sets, broker materials, data rooms, filings, credit agreements, expert call transcripts, and answers multi-step questions in a table with a citation behind every cell. It connects to the major market data and expert networks, FactSet, S&P Capital IQ, PitchBook, Bloomberg, and Third Bridge among them, pulls governed deal content from SS&C Intralinks data rooms, and since the FlashDocs acquisition it turns analysis into slides.

Then the deal moves, and the work changes shape. Hebbia will generate a financial model as an Excel export from what it has read, but a third-party evaluation puts the limit plainly: the platform does not run the formulas itself, so a derived figure carries a chain from claim to source but not the math underneath it. It will draft an investment committee memo from the corpus, but a CRE committee reads the memo off the model, return stack first, and there is no model in the grid to read. And nothing in Hebbia’s materials describes a rent roll ingested unit by unit, a T-12 normalized onto standard lines, or property-level underwriting of any kind.

Past the IC the trail goes quiet. Hebbia’s diligence content stops at memo generation; its public materials describe no settlement reconciliation, no closing record, and no tracking of actuals against the original underwrite through the hold. It runs as a shared platform, with strong certifications but no published per-customer isolated deployment. Third-party sources put the professional tier near $10,000 a seat per year. For a CRE investment team that is a research subscription, not a deal team, and that gap is what this shortlist is for.

The frame

Five questions that sort the field.

Every platform in this market reads documents well and writes fluently. The differences that matter sit one level down: whether the tool answers questions about the documents or works the deal file itself, and whether the output survives the committee. Five questions sort the field faster than any feature list.

Run those five against any vendor on this page and the marketing falls away quickly. The entries above are written in those terms: what each one genuinely does well, and where the work moves back onto your analysts.

Document depth. When a figure lands in the work, can the analyst follow it back to the exact file, sheet, and row or page, and does it foot to the document’s own stated totals, or does the citation stop at the document?
Model building. Does the tool build a workbook with live formulas the committee can interrogate, does it export a synthesis into Excel, or does it populate a model the firm already keeps?
Memo voice. Does the memo read like the house wrote it, with every figure pulled from the model or a cited source, or is it a capable generic draft someone rewrites?
Lifecycle coverage. Where does the tool stop: at research, at the IC, at the wire, or does it carry through closing reconciliation and the hold?
Deployment isolation. Does the work product sit on a shared platform, or walled off in an environment scoped to the firm, with deal-level separation inside it?

The buyer’s read

Where Hebbia still wins, and how to choose.

Hebbia keeps the job it was built for. When the question runs across forty data rooms or a thousand credit agreements and the answer needs a citation behind every cell, the grid is the right tool, and nothing else on this page reads at that scale. That ground is real. It is also research ground, not deal ground.

So choose by the job in front of the team. If the binding constraint is market intelligence, broker research, and expert calls, that is AlphaSense’s corpus. If the firm’s work is sell-side and advisory, Rogo is built for that team. If you want one assistant across private-markets knowledge work, BlueFlame AI covers the drafting and synthesis, with Datasite behind it. If your analysts keep their own proformas and want the documents read into them faster, V7 Go does that one step, with a person approving each pass.

And if the job is the deal itself, that is the job Cap Orbit was built around. The terminal is not a document chat layer: working a deal on it reads like getting back the workbook, memo, and record, the real Excel workbook with live formulas, the Word memo in the firm’s format and voice, the PowerPoint deck, the bound PDF, with the analyst approving each consequential step on the way through. It carries the closing trued to what actually funded and the hold tracked period by period against the original basis. It produces the work; your committee keeps the decision. Pro puts a fund of up to 50 people on live deals within 24 hours; Enterprise runs the same platform deployed into the firm’s own cloud account. The evaluation is a working session on one of your live deals, run end to end in your own formats.

Common questions

Do any of these alternatives read rent rolls and T-12s the way a CRE analyst needs?

Two of the five. Cap Orbit pulls the rent roll unit by unit out of whatever the broker sent, even buried in a workbook tab or a scanned exhibit, with every figure traced to its exact source and footed to the document’s own totals, and normalizes the T-12 onto standard expense lines. V7 Go reads offering memorandums and populates Excel or ARGUS proformas with human review gates. Nothing in the public materials of Hebbia, Rogo, or AlphaSense describes rent roll or T-12 handling.

Which alternative actually builds the underwriting model?

Cap Orbit builds the workbook itself: live formulas, no hardcodes, scenarios off one switch, recalculated and checked before delivery, either to an institutional default or into the firm’s own template in place. Rogo builds and maintains Excel models through its Subset acquisition, but for corporate finance work; its materials describe nothing property-level. AlphaSense’s modeling centers on public-company models. Hebbia exports a model from document synthesis and, per third-party evaluation, does not run the math in the platform.

We rely on market data and expert calls. Which way does that cut?

Cap Orbit’s data source is the deal folder. Drop any document in any format onto the deal, broker materials, lender PDFs, scanned pages, spreadsheets, exactly like a real deal folder, and it reads them all at once. It carries no third-party market data or expert-call subscription, so if the binding constraint is what the street and the experts say, Hebbia’s market data connections and AlphaSense’s corpus serve that job. Many teams run one of each; the question is which tool owns the deal record.

How does Cap Orbit keep one firm’s deals away from everyone else’s?

Each firm runs walled off in its own environment: its own database with writer privileges scoped to that database alone, its own document storage, nothing pooled across customers. Inside the firm, each deal runs in its own dedicated compute with only that deal’s files attached, so the team working one transaction cannot see another’s materials. Access is brokered and short-lived, re-checked against the firm’s own sign-in, and customer files, prompts, and outputs are never used to train any model. On the Enterprise tier the same platform deploys into the firm’s own cloud account, with single sign-on and customer-held keys, so every resource and access path is auditable from the firm’s side.

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