The alternatives to Model ML worth knowing are Inven, which produces company one-pagers and overview slides in your firm's own PowerPoint template from data Inven owns; Rogo, a finance agent working over the licenses a bank already pays for — LSEG, FactSet, Capital IQ, PitchBook, and Preqin — plus the firm's files; Hebbia, which reads a data room, a CIM, or a pile of filings and answers with citations; Microsoft Copilot, which drafts inside Word, Excel, and Outlook; and ChatGPT or Claude for writing when you already have the facts. Model ML's own job is AI inside Office — slides, Excel, and its Notetaker, fed by connected providers — so the right alternative depends on which part of that you are trying to cover, and on whether the gap you feel is actually a job Model ML was never doing in the first place.

Which Model ML alternative fits which job?

Match the tool to the gap: Inven if what you need at the end is the company page, Rogo for analytical questions over licensed data, Hebbia for document piles, Copilot for general drafting inside Office, and the raw models for writing from facts you already hold.

Is Inven a good alternative to Model ML?

For one specific and expensive part of the work, yes: the company page. Give Inven a company and it generates 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). That is a different architecture from Model ML's, which draws facts from connected providers; Inven does not sit on PitchBook, Capital IQ, or FactSet as its company universe, so the profile does not depend on which providers the firm has plugged in or how deep their coverage runs.

The setup is a saved task the whole team shares — define the template and fields once, and the next person types the next company into the same task. The boundary, stated plainly: Inven can't yet make a full CDD deck, pitch book, CIM, or teaser, but it shines in the individual slides and analyses that are parts of them. And this is an alternative for the page, not for the rest: Inven does not replace Model ML's Notetaker or the Excel model work. Teams run the two together — two jobs, not a swap. To see the page come out of your own template, book a demo.

Is Rogo a good alternative to Model ML?

It is the closest competitor for the same budget line, with a different center of gravity. Where Model ML weaves AI through Office documents, Rogo is an agent you ask: analytical questions answered over the licenses the bank already pays for, plus the firm's own files. If what your team wanted from Model ML was really "make our licensed data easier to use," Rogo attacks that directly, and its answers inherit the credibility of the feeds underneath.

What you give up is the in-document placement. Rogo is not living inside your slides and spreadsheets the way Model ML does, and neither of them owns a company universe — both are intelligence layers over data that comes from somewhere else. Choosing between them is mostly choosing where you want the AI to sit: inside the documents, or behind a question box.

Is Hebbia a good alternative to Model ML?

Only if your real workload is documents. Hebbia's job is reading at volume — load a data room, a CIM, or a stack of filings, ask questions across all of it, and get answers with citations pointing to the exact page. For diligence-heavy teams, that traceability is worth more than any drafting convenience, and it is a job Model ML does not do at that depth.

The boundary is the same one that defines it everywhere: Hebbia works on the documents you load, brings no company data of its own, and produces understanding rather than deliverables. It replaces the reading, not the writing or the slide.

Can Microsoft Copilot replace Model ML?

For general drafting inside Office, partly — and it is probably already in the building. Copilot drafts in Word, works in Excel, and summarizes in Outlook, and for ordinary document work its convenience is real. If the team's use of Model ML never went past "help me write this faster," Copilot covers much of that at the cost of a license most firms already hold.

What Copilot lacks is everything finance-specific. There are no data providers behind its answers, no notion of a deal workflow, and nothing like the Notetaker's meeting capture feeding back into the work. It knows Office; it does not know finance data. Treat it as the general-purpose floor, not the replacement.

Can ChatGPT or Claude replace Model ML?

They cover the writing, nothing else. Hand either model verified facts and it drafts a clean paragraph faster than anyone on the desk — that is a real job and they are the cheapest way to do it. But there is no company data behind them, no integration with the documents the team works in, and nothing saved: every use starts from a blank prompt, and the second analyst inherits nothing from the first. Ask them about a company cold and the confident answer may be invented. Use them at the end of the process, on facts already gathered, and expect to build the slide yourself.

Do you need to replace Model ML at all?

Often not. If the Notetaker is capturing your meetings and the Office assistance is polishing work on companies the team already covers, those jobs are being done — keep them. The better question is which job is still being done by hand, and for most deal teams that is the company page: the profile rebuilt from scratch every time a name comes up, twenty times a quarter.

That job does not belong to Model ML, and filling it is addition, not replacement — Inven producing the one-pagers and overview slides in the firm's template from data it owns.

If that is the gap costing your team its evenings, book a demo and bring the last profile your team built manually; the honest test is watching the same page come out of a saved task in your own template.