ApexMake vs AlphaSense
AlphaSense is the best product in the world for reasoning over documents that already exist. ApexMake exists for the knowledge nobody ever wrote down.
- Corpus: 500M+ documents that already exist, against knowledge created by interview
- Reach: market-wide breadth, against depth in AI infrastructure
- Citations: to a source document, against to a named operator
The short answer
- AlphaSense is an AI-powered market intelligence platform: more than 500 million documents, 1,500+ broker research partners, 4,000+ financial models, and after acquiring Tegus for $930M in 2024, an expert transcript library of 280,000+ interviews across 29,000+ companies growing by roughly 8,000 transcripts a month.
- Its AI stack goes deep. Generative Search returns analyst-level answers with citations, Deep Research agents automate multi-step research, and it has shipped an autonomous AI Agent Interviewer alongside channel checks.
- The defining property is that AlphaSense is document-first. Its corpus is text that already exists, filings, broker research, news, past expert calls, and its AI reasons over that corpus very well.
- ApexMake is people-first. Its corpus gets created by having a proprietary AI agent conduct deep structured interviews with vetted operators, which lets it answer questions that were never documented, with citations to the named expert and a median time to answer of about six seconds. Synchronous calls with those same operators are part of the subscription.
- AlphaSense is the research platform of record across the whole market. ApexMake is for when the answer depends on lived operating experience that no filing, broker note or past transcript contains.
Which should you use?
These are the two most AI-forward products in investment research, and they complement each other more than they compete. AlphaSense has assembled the definitive corpus of things written about companies and made it queryable with strong AI. ApexMake handles the residual: knowledge that exists only in operators' heads because nobody ever had a reason to write it down. If your question can be answered from documents, AlphaSense will answer it faster and across more of the market than anything else. If it turns on real lead times, vendor politics, or why a contract actually churned, no document corpus contains it, and that gap is what ApexMake was built for.
What sets ApexMake apart
| Dimension | ||
|---|---|---|
| Corpus | Expert knowledge created on purpose, through AI-led interviews with vetted operators | Documents that already exist: filings, broker research, news, past expert transcripts |
| Primary job | Answer questions no document contains | Find and synthesise everything that has been written |
| Breadth | Deep by design: AI infrastructure, inference, training, 1,000+ software and infra products | Very broad. 29,000+ companies for expert content, effectively the whole market for documents. |
| Time to answer | About 6 seconds (median) to a cited, synthesised answer | Seconds for generative search, minutes for Deep Research agents |
| Freshness of expert content | Captured and refreshed continuously from people | Transcripts fixed at their recording date, with roughly 8,000 new ones a month |
| Follow-ups | Conversational, against live expert knowledge | Conversational, against the document corpus |
| Attribution | Citations to the named expert behind each claim | Citations to the source document |
| Live expert calls | Included. Book a pre-vetted operator in a couple of clicks, with their previous answers visible first. | AI-led or human-led expert calls, priced separately |
| Compliance record | MNPI screening plus a timestamped audit trail on every query and follow-up | AI-driven compliance scanning of expert content before publication |
What AlphaSense is
- 2011, New York
- Subscription AI market intelligence platform. Document search, generative AI research agents, and an expert transcript library with paid expert calls.
- 500M+ documents, 1,500+ broker research partners, 4,000+ financial models, and 280,000+ expert transcripts across 29,000+ companies with roughly 8,000 added monthly.
- $4B, following a $650M raise alongside the $930M Tegus acquisition in 2024.
- Reported at roughly $10K to $20K per seat annually, with enterprise deals commonly $50K to $100K and up, and the largest customers above $1M. Expert calls are priced separately, with flat-fee 1:1 calls advertised.
- Generative Search with source citations, Deep Research agents, AI summaries and sentiment, an autonomous AI Agent Interviewer, channel checks, and AI compliance scanning.
Document intelligence has a hard ceiling, and it is not a software problem
AlphaSense has pulled off something remarkable. More than 500 million documents assembled, broker research from 1,500+ partners added, the largest expert transcript library in the industry absorbed, and a capable generative layer put on top of all of it. If the answer to your question has been written down anywhere, AlphaSense will find it, cite it and summarise it.
The ceiling is not the AI. It is the corpus. A retrieval system cannot return what was never recorded, and the most decision-relevant facts in private markets are the ones nobody recorded: the real lead time versus the quoted one, which integration breaks at scale, the political reason a vendor relationship soured, what the churned customer said in the room.
That information exists. It sits in a small number of people's heads. Public information became a commodity because AI research agents surface the same public data for every fund, and what is left of the edge lives in exactly the material document intelligence cannot reach.
Creating the corpus versus indexing it
ApexMake manufactures its corpus rather than indexing one. Operators, engineers and executives with senior first-hand experience get recruited, screened for expertise and conflicts, then interviewed in depth by a proprietary AI agent. The interview is structured and thorough in a way no human interviewer could sustain at scale.
What comes out is a queryable representation of lived experience: the kind of thing that would otherwise mean booking the person and hoping your analyst asks the right question inside fifty minutes.
On the surface both approaches then look similar. You type a question, you get a cited answer. The difference is what the citation points at. AlphaSense cites a document. ApexMake cites a person who did the thing.
Both companies now run AI interviewers, for different reasons
AlphaSense has launched an autonomous AI Agent Interviewer and channel-check capability, letting clients run interviews at scale to test a thesis and feed results back into the platform. GLG has shipped AI-moderated calls in ten languages. The industry has converged on the idea that an AI can run a competent expert interview.
Where they differ is what the interview is for. In a platform model, the AI interviewer is a way to generate more transcripts on demand, more documents for the corpus, commissioned per client project.
In ApexMake's model, the AI interview is how the network itself gets built, ahead of demand and refreshed continuously. Which is why median time to answer is six seconds rather than the length of an interview project. The interviews already happened, and every question after that benefits from them.
Depth in a domain versus breadth across the market
AlphaSense's expert content spans 29,000+ companies and its document coverage is effectively market-wide. No specialist network competes with that on breadth, and none should try.
ApexMake concentrates instead: deep coverage of AI infrastructure, inference and training, plus more than a thousand software and infrastructure products. In those areas the questions deal teams actually ask are specific enough that market-wide coverage is not the binding constraint. Density of properly qualified operators is.
So the two fail in opposite places. A broad platform thins out exactly where a technical question gets sharp. A specialist network has nothing to say about a sector it does not cover. Knowing which failure you are more exposed to is most of the buying decision.
Auditability of an AI answer
Once AI is generating research output, compliance stops asking whether the call was chaperoned and starts asking whether you can reconstruct how the answer was produced.
AlphaSense applies AI-driven compliance scanning to expert content before it enters the library, which handles the content side of the problem.
ApexMake logs the interaction side: query received and screened, experts matched and conflict-checked, answer delivered with citations, follow-up screened again, each one timestamped and recorded. For regulated capital an answer you cannot defend is worse than no answer, and that principle is easier to meet when the audit trail is produced automatically rather than assembled after the fact.
Choosing between them
AlphaSense fits better when
- You need one research platform of record across filings, broker research, news, transcripts and financial models.
- Coverage breadth across tens of thousands of companies matters more than depth in a single technical domain.
- Much of your work is document-grounded: reading earnings calls at scale, tracking sentiment, building comparable sets.
- You want AI agents automating multi-step desk research over an existing corpus.
ApexMake fits better when
- The answer depends on lived operating experience no filing, broker note or transcript ever captured.
- Your questions cluster in AI infrastructure, inference, training or the software and infra stack around them.
- You need citations to a named expert with relevant first-hand experience rather than to a document.
- You want to see where operators disagree, since the divergence is often the finding.
- Compliance wants a machine-readable audit trail of every query and answer, not only content-level screening.
- You want expert calls in the same subscription as the answers, aimed at investor questions rather than desk research.
ApexMake vs AlphaSense, explained
Is ApexMake a competitor to AlphaSense?
Only partly. AlphaSense is a document intelligence platform with an expert transcript library attached. ApexMake is an expert network built AI-first. They overlap on expert content and diverge everywhere else. Most firms using both treat AlphaSense as the platform of record and ApexMake as the answer layer for technical questions no document covers.
How large is AlphaSense's expert transcript library?
AlphaSense reports more than 280,000 expert transcripts across 29,000+ companies, with roughly 8,000 added each month. That includes the Tegus Expert Transcript Library acquired in the $930M Tegus deal in 2024.
AlphaSense has AI agents and citations too, so what does ApexMake do differently?
The difference is the corpus, not the interface. AlphaSense's agents reason over documents that already exist and cite those documents. ApexMake's answers are generated from structured AI-led interviews with vetted operators and cite the named expert behind each claim, which lets it answer questions nobody ever wrote down.
What does AlphaSense cost?
Publicly reported figures put seat pricing at roughly $10,000 to $20,000 a year, with enterprise deals commonly $50,000 to $100,000 and up, and the largest customers above $1M. Expert calls are priced separately, with flat-fee 1:1 calls advertised.
Do I still need expert calls if I have ApexMake?
Less often, and when you do they are part of the product. Calls with pre-vetted operators book in a couple of clicks, with the operator's previous answers visible before you choose, all inside the same subscription.
Sources
Figures describing AlphaSense are drawn from the company’s own published materials and third-party reporting, and were accurate as of the date of publication. Product and pricing details change; verify with the provider before making a purchasing decision.