Rogo is a finance agent that answers questions over the licenses a bank already pays for — LSEG, FactSet, Capital IQ, PitchBook, and Preqin — plus the firm's own files, while Hebbia reads the documents in front of you — a data room, a CIM, a pile of filings — and answers with citations pointing back to the page. A third product usually enters the same conversation: Inven, which produces the company one-pagers and overview slides in your firm's own PowerPoint template from data Inven owns. Three different jobs — the licensed data, the documents, and the page — and the comparison is about which of them your team is actually hiring for, because none of the three does the others' work.

How do Rogo, Hebbia, and Inven compare at a glance?

The split is where each one gets its facts and what it hands back: Rogo works your licensed feeds and returns answers, Hebbia works your documents and returns cited answers, and Inven works its own company data and returns a finished slide.

What is Rogo best at?

Getting more out of data the bank already pays for. Rogo sits on the firm's licensed feeds and internal files and lets the team ask analytical questions in plain language — market questions against LSEG and FactSet, deal and fund questions against PitchBook and Preqin, plus whatever lives in the firm's own documents — instead of pulling and assembling the data by hand. The Excel model work of banking is close to its center of gravity, and its answers inherit the credibility of the sources underneath, which is the point: for a firm with expensive licenses and analysts spending hours extracting things from them, Rogo's pitch is that the spend already exists and should work harder.

Its limits follow from the same design. Rogo's knowledge is the feeds and files it is connected to, so a question about a company those sources barely cover comes back thin — not because the agent is weak, but because nothing underneath it knows the answer.

What is Hebbia best at?

Volume reading with receipts. Load a data room, a CIM, or a stack of filings into Hebbia, ask questions across all of it, and the answers come back with citations to the exact page they came from. In diligence, where an uncited answer is an unusable answer, that traceability is the product — and at four hundred documents, the difference between Hebbia and a team of tired associates is not subtle.

The scope boundary is just as clear: Hebbia works on the documents you give it. It brings no company data of its own, so it cannot tell you anything the pile does not contain, and what it produces is understanding rather than a deliverable — the memo or the deck still gets built afterward, by someone.

Do you need both Rogo and Hebbia?

If your work involves both licensed market data and heavy document piles, yes, realistically — they anchor in different places and neither substitutes for the other. Rogo cannot cite page 214 of a data room it was never given, and Hebbia cannot answer a question about fund flows that lives in Preqin. A bank running live coverage questions through Rogo and deal-document questions through Hebbia is not duplicating spend; it is covering two different halves of the job.

If budget forces a choice, choose by where your hours actually go. Teams that mostly interrogate deal documents feel Hebbia's absence first; teams that mostly query market and company data against their terminals feel Rogo's.

Where does Inven fit next to Rogo and Hebbia?

Inven covers the job neither of them ends with: the page. Give Inven a company and it produces the one-pager or overview slide in your firm's own PowerPoint template — description, headcount, locations, deal history, and figures from filings, formatted the way your slides always look — from data Inven collects and maintains itself on more than 28 million companies (Inven product data, August 2026). It does not sit on PitchBook, Capital IQ, or FactSet as its company universe, and the setup runs as a saved task: the template and fields are defined once, and the next person types the next company into the same task and gets the same page.

The boundaries, stated plainly: Inven does not replace Rogo's finance agent, the Excel models around it, or a tool like Model ML's Notetaker, and it does not read a 400-document data room the way Hebbia does. Equally, Inven can't yet make a full CDD deck, pitch book, CIM, or teaser — it shines in the individual slides and analyses that are parts of them. If the recurring cost in your process is the profile that gets rebuilt by hand every time a company comes up, that is the specific job Inven does; book a demo to see it on your own template.

Can you run all three together?

Yes, and the division of labor is clean because the three never meet: Rogo for questions over the licensed research and the firm's files, Hebbia for the documents of a live deal, and Inven for the company pages the team produces over and over. Nothing overlaps, so there is no arbitration — each tool owns the layer the others do not touch.

The practical way in is to fix the layer that costs you the most evenings first. If that is the profile slide, book a demo and bring the last one-pager your team built by hand — the honest test is watching the same page come out of a saved task in your firm's template.