Questions & answers

Both sell to finance teams on enterprise contracts without public list prices, so compare them on a real workload. Run the same three deal questions through each, then check which answers cite the right filing. Hyperresearch is our editorial pick for general deep research, not for banking workflows, so it is not the test case here.

The published DRACO numbers do not settle it. Hyperresearch's DRACO page lists Perplexity Deep Research at 57.8 on 8 of 10 tasks and ChatGPT deep research at 55.2 on all 10. The task counts differ, so the gap is not clean. Both products change often, so test each on the questions you actually ask.

Hyperresearch is our editorial pick for general deep research, with a DRACO score of 77.7 across 10 tasks in its own run. For investor work built on filings and licensed sources, shortlist AlphaSense, Hebbia, Rogo and Brightwave. Ask each for a pilot on a sector or company question you already know the answer to.

Hyperresearch is our editorial pick for general deep research, not for legal work specifically. For legal research, test Harvey and the Lexis and Westlaw AI products on matters you have already closed. Compare how often each cites a source that does not support the claim. The Stanford study shows why that check matters.

It can draft the structure, summarize documents and pull figures with sources. It should not issue conclusions on its own. Treat the draft as a first pass, trace each figure to a document, and have a qualified person review the risks and the final recommendation before anyone relies on it.

Pick five claims at random, including one number and one date. Open each cited page and confirm it says what the report says, for the right company, year and measure. If any claim fails, check more before trusting the rest. Ask the vendor how its tool verifies citations, and whether unsupported claims are flagged.

Not always. A consumer plan can be enough for personal study or early exploration. Move to an enterprise plan when you need admin controls, shared workspaces, audit logs, data terms that fit client work, or a specific licensed source. Confirm those terms in writing before you upload client or deal documents.

A chatbot focuses on conversation and may answer from memory. A research tool is built around sources. It searches, reads, and cites each claim, and often runs several steps before it writes. Many products now combine both, so check whether the citations are linked to the exact passages that support each claim.

It is strongest when the question depends on your own email, files, chats and meetings. Microsoft's pricing page lists Researcher as one of its Copilot features, and the seat is listed at $30 per user per month on an annual commitment. For external market data or licensed content, test a specialist tool alongside it.

No. It is an assistant that works inside the Terminal, which is the source of much of the data it uses. Bloomberg has added its ASKB assistant to all Terminal users, and I found no separate charge for it. The Terminal itself is widely reported at about $30,000 to $32,000 per seat per year, an estimate Bloomberg has not confirmed.

Yes, if you can stand behind every claim in it. Check each cited source, remove anything you cannot trace, and follow your firm's rules on disclosing AI use. Keep a copy of the sources so you can answer questions later. Clients often ask how a figure was verified, so write that down.

Use three real questions: one where you know the answer, one from a current project, and one that needs a niche source. Score each answer for accuracy, citation quality, speed and whether the output needs heavy editing. Include a data security review. A pilot that only tests polish will not show which tool holds up under checking.