AlphaSense vs Rogo

AlphaSense and Rogo both sell AI to finance, and they start from opposite ends. AlphaSense is a research library first: 500M+ documents, broker research and expert-call transcripts, with an agent that reads across them. Rogo is a banker’s agent first: hundreds of agents that turn a prompt into a finished deck, model or document on licensed feeds. For firms whose numbers go to an investment committee or a client, there’s a third option in Kepler, which builds a model of the firm and computes every figure in code with a citation to the line.

We build Kepler. Facts about AlphaSense and Rogo come from their own websites, press releases and public reporting as of November 2026, linked inline.

The short answer

• Choose AlphaSense if your team reads more than it builds: broker research, expert calls, transcripts and filings across many companies, with an agent that turns them into memos and dashboards.
• Choose Rogo if your team builds banker deliverables all day and wants an agent to produce pitch books, comps, precedents and models in the bank’s template from one prompt.
• Consider Kepler if the numbers in those memos and books have to hold up: each one traced to its filing line, on your own definitions, the same every time you ask.

AlphaSense vs Rogo vs Kepler at a glance

	AlphaSense	Rogo	Kepler
Built around	A library of 500M+ licensed and owned documents	Agents that produce banker deliverables	Your firm’s ontology and a deterministic engine
Main users	Research teams on the buy side, sell side and in corporates	Investment banks, plus PE, credit, hedge funds and asset managers	Hedge funds, PE, credit, asset managers, allocators and deal teams
Content it brings	Broker research, Tegus expert transcripts, Canalyst models, filings	Licensed feeds through 51 listed integrations	26M+ SEC filings with XBRL, 40M+ IR documents and transcripts, market data, news
Your firm’s data	Connectors for CRM, email, chat and files	SharePoint, DealCloud, data rooms; Rogo Intelligence	Every source read in full and resolved into the ontology
Agent	SuperAnalyst	Felix and the Agent Library	Agents on schedules and events, acting as their owner
Office output	PowerPoint and Excel add-ins; decks up to 40 slides	Finished Excel, PowerPoint and Word files	Excel with live formulas, PowerPoint and Word in house format
Where numbers come from	AI answers over library content and its financial data; SuperAnalyst writes and runs code	Built by agents from connected feeds	Pulled from the source and computed by code
What a click opens	The cited passage in the source document	The linked source	The exact page, line or cell, with formula and inputs
Same question twice	Not stated publicly	Not stated publicly	The same number, from the same lines
As filed vs restated	Not stated publicly	Not stated publicly	Either, on any date
Also works in	AlphaSense app	Rogo app, Copilot in Excel, email	Claude, ChatGPT, Slack, email, API, MCP

What AlphaSense is

AlphaSense is a market intelligence platform whose center of gravity is content. It searches 500M+ documents across broker research (its Wall Street Insights collection), regulatory filings, earnings call transcripts, trade press and expert calls; its acquisition of Tegus brought in Tegus Expert Insights and Canalyst models. SuperAnalyst, its agent, entered early access in June 2026 and got a major release on October 6: it plans the work, reconciles evidence, writes and runs code, and produces memos, models, comparison tables and dashboards that refresh themselves. Pre-built workflows include buyer universe development, earnings previews, forecasting and competitive mapping.

For bankers, AlphaSense has moved closer to Rogo’s ground. Its PowerPoint and Excel add-ins (July 13, 2026) build decks of up to 40 slides in a firm’s template, update comps tables and precedent transactions, and, for early beta customers, build LBO, DCF and M&A models from firm templates. Its banking page adds buyer and target search across 40M+ entities and in-document analysis of CIMs and data rooms. AlphaSense doesn’t publish prices.

What users say: “The AI does a good job of handling one document at a time but often delivers ineffective results if searching for insights over a longer timeframe,” wrote a TrustRadius reviewer on an investing team.

What Rogo is

Rogo is an AI platform built around banking work. Felix, its financial agent harness, runs an Agent Library of hundreds of agents across investment banking, private equity, private credit, hedge funds, equity research and asset management, each returning a finished Excel model, PowerPoint deck or Word document from one prompt. Banking agents cover fairness-opinion-style DCFs and LBOs, public comps, precedent transactions, CIM analysis and PIBs, and teams adapt them with their own precedents and house formatting.

Rogo’s data arrives mainly through partners: 51 sources on its integrations page, including S&P Capital IQ, FactSet, LSEG, PitchBook, Daloopa and Preqin, which went live in Felix in August for buyer, sponsor and LP target lists. In 2026 it bought Offset, the Plux team, Rivanna for data-room diligence and Arvo for meetings, launched Deal Room, and introduced Rogo Intelligence, which it calls the firm’s context layer. Model Broker routes each task to a model by cost and quality. Rogo doesn’t publish prices; Sacra puts them at about $3,300 per seat annually.

What users say: “If I have it try a task or give a shot at an analysis, its a lot of coaching, only for it to miss a simple thing, or not present it in a way I want (despite asking explicitly),” wrote a vice president in investment banking on Wall Street Oasis.

Where they overlap, and where they don’t

Pitch materials. Both now produce decks in a firm’s template. Rogo’s agents start from the deliverable; AlphaSense’s add-ins start from a research session. Rogo is further along on the full banker set (DCF, LBO, comps, precedents, PIBs in one agent library).

Content. This is the clearest split. AlphaSense owns an expert-call library and Canalyst models and aggregates broker research at scale. Rogo brings breadth of licensed market and deal data, much of it on the firm’s own subscriptions, and reaches expert content through partners such as Third Bridge.

Firm context. Both have moved toward the firm’s own data: AlphaSense through connectors that bring CRM, email, chat and files into its search, Rogo through Deal Room and Rogo Intelligence, which codifies a firm’s expertise for reuse.

Numbers. In both, an agent assembles figures from sources and cites them. Ask each whether the same request returns the same number, whose adjusted EBITDA a multiple uses, and whether a figure is shown as filed or as restated, then test both on your own peer set.

The third option: Kepler

We build Kepler.

Both products answer the question “what does the market say?” well. Kepler is built for a different question: “what is the number, on our definition, and can we prove it?”

A frontier model on its own writes numbers as text, so a multiple can be wrong in a way nobody sees, and it knows nothing about your firm: your coverage list, your funds, your definition of adjusted EBITDA, your prior deals. We built Kepler to close both gaps. Our forward-deployed engineers, who built this layer before at Palantir, build your firm’s ontology from your own documents and systems, so every company, fund, deal, manager and person resolves to one record and every house term carries one definition. Extraction models we train have already read the public record in full: 26M+ SEC filings across 20+ form types and 40M+ investor relations documents and earnings call transcripts for 14,000+ companies in 120+ countries, with each fact stored beside its passage.

Code computes every figure, and the model never writes one. Statements assemble from each filing’s XBRL, as filed or as restated. Comps use each company’s own adjusted EBITDA from its published reconciliation, summed over four quarters in code, and every multiple opens to its formula and cited inputs. Citations are checked against their sources while the answer is written, and conflicting sources appear side by side. Agents run on schedules and events, such as a peer’s 10-Q or a guidance change, acting as their owner. The results land in Excel, PowerPoint and Word in your format, inside Claude and ChatGPT, in Slack and email, or in your own systems through our API. One permission check governs every read, every query is logged with its lineage, and your playbooks run the same way every time. We don’t carry expert-call transcripts or broker research, which are licensed content, so teams that read them keep a library for that work.

Not the right fit if: your team’s main need is reading broker notes and expert interviews, or producing high volumes of pitch pages where the numbers come from a licensed feed you already trust.

“I can’t put a number in front of a client if I can’t show where it came from.” (Managing Director, multi-billion dollar fund)

The same mandate in each

A coverage team takes on the sale of a founder-owned vertical software company and asks for “the strategic buyer universe, trading comps for listed peers on EV/EBITDA, and a three-page market section for the pitch.”

In AlphaSense, SuperAnalyst’s buyer universe workflow ranks strategics from the library, expert calls add color on the sector, and the PowerPoint add-in builds the market pages in the bank’s template, with comps updated through the Excel add-in.

In Rogo, a comps agent and Felix pull peers and multiples from connected feeds such as S&P Capital IQ or FactSet, Preqin and PitchBook add sponsors, and the agent returns a finished deck in the bank’s format.

In Kepler, each listed peer is one record in the public ontology, and its adjusted EBITDA comes from its own reconciliation, computed in code with every quarter cited. Strategic buyers rank by what their filings show: acquisitions disclosed in 10-Ks and 8-Ks, cash, debt capacity computed from the balance sheet, and segment overlap, each cited. The bank’s relationship history comes from its CRM, already in the firm’s ontology. The market section’s numbers open to their sources, and an agent refreshes the comps page when a peer reports.

In any trial, ask each tool whose adjusted EBITDA a multiple uses, click ten cells, and run the request twice.

How to choose

1. Name the main job. Reading the market, producing the deliverable, or proving the numbers in it.
2. Test with a live request. Use a real peer set or a recent mandate, not the vendor’s demo.
3. Check your licenses. Count which data each tool brings and which runs on subscriptions you already pay for.
4. Click the numbers. Each figure should open its source, period and units. Our guide to checking AI-generated numbers has the full list.

For each product in more depth, see AlphaSense alternatives, Rogo alternatives, Rogo vs Hebbia and Kepler vs Rogo. For banking specifically, see the best AI tools for investment banking.

Ask Kepler for comps on your own peer set at app.kepler.ai, or book a demo.

Frequently asked questions

What is the difference between AlphaSense and Rogo?

AlphaSense is a market intelligence platform built around content it licenses or owns: 500M+ documents, broker research, expert-call transcripts and financial models, searched by AI and worked by its SuperAnalyst agent. Rogo is an AI platform built for bankers: hundreds of agents turn a prompt into finished Excel, PowerPoint and Word files using 51 listed integrations with licensed data. Kepler is a third option, built around a firm's own data with every number computed in code.

Is AlphaSense or Rogo better for investment banking?

Rogo is the closer fit for producing banker deliverables: pitch books, comps, precedents, DCFs and LBOs from one prompt, on the bank's templates. AlphaSense is stronger for sector research, expert calls and broker views, and its PowerPoint and Excel add-ins now build pitch pages too. For multiples a client will see, Kepler computes each one in code from cited filing lines.

Which is better for hedge funds and the buy side?

AlphaSense, if the job is reading broker research, expert calls and transcripts across many companies. Rogo offers agents for hedge funds and private equity, though its product grew up around the banker's deliverable. Funds that need AI built around their own holdings, definitions and documents, with numbers that reproduce, should look at Kepler.

Do AlphaSense and Rogo use the same data?

Partly. AlphaSense owns much of its content, including Tegus expert transcripts and Canalyst models, and licenses broker research and other sources. Rogo connects to providers such as S&P Capital IQ, FactSet, LSEG, PitchBook, Preqin and Daloopa, often through the firm's own licenses. Both connect to a firm's internal files, such as SharePoint.

What is an alternative to both AlphaSense and Rogo?

Kepler, for firms whose answers end up in a model, an IC memo, an LP report or a client book. Our engineers build the firm's ontology from its own documents and systems, every SEC filing and transcript is read in full before anyone asks, and code computes and cites every number to the exact page, line or cell.