AlphaSense vs Hebbia vs AllMind: Choose by Workflow

Choose AlphaSense when its specific licensed content package, Tegus expert library, and enterprise search are the primary assets. Choose Hebbia when analysts need to apply repeated questions across a visible document or company grid. Evaluate AllMind when licensed institutional data, internal structured data, and recurring research artifacts must share one workflow. All three now advertise agents, so the decisive difference is the contracted evidence and operating model around those agents.

This is a documented comparison based on public product pages and documentation checked on August 30, 2026. We did not run the same task in the three products. One of the three is ours: we build AllMind, which is a direct conflict of interest. Statements about it are our first-party claims, and no universal product verdict is supported here.

The three product centers

Feature lists obscure the main difference. Each product gives a different object to the analyst at the start of the work.

• AlphaSense starts with a content universe. Premium external research and, in Enterprise Intelligence, proprietary internal content become searchable and available to AI workflows.
• Hebbia starts with a work surface. Matrix exposes a universe of documents or companies as rows and a set of repeated research operations as columns.
• AllMind starts with connected financial entities and an institutional data corpus. Its ontology links companies, securities, people, documents, licensed provider data, and the firm's information.

This table records advertised facts. It does not establish output accuracy, retrieval completeness, or analyst time saved.

AlphaSense: content depth plus an expanding workflow layer

AlphaSense's package page is the clearest public evidence of its content position. It lists company documents, regulatory material, news, broker and independent research, and an expert transcript library. The Expert Insights page reports roughly 300,000 investor-led insights. Enterprise Intelligence connects internal files and applies search and generative tools under enterprise controls.

It would be outdated to describe AlphaSense as a search-only product. The company's June 2026 release notes describe Organizational Agents, user-uploaded presentation templates, added connectors, and an Intralinks VDRPro connection. Its developer portal also advertises an Agent API and MCP tools.

The strongest evaluation case is therefore source-led. Give AlphaSense questions that require a licensed broker note, an expert interview, a filing, and an internal memo. Verify whether all required sources are present, permissioned, cited, and available to the chosen workflow.

The principal unknown is commercial scope. AlphaSense offers annual per-seat through enterprise-wide arrangements but no public rate card. The proposal must identify content packages, add-ons, services, and internal-content deployment assumptions.

Hebbia: make the research process visible in a grid

Hebbia's interface is easier to understand through a repeated task. Put a set of companies or source documents into rows. Define research operations in columns, such as extracting covenant terms, identifying segment disclosures, or recording valuation inputs. Matrix applies those operations across the universe and exposes citations for review.

The current Hebbia site depicts filings, earnings transcripts, financial data, and firm knowledge in finance workflows. That matters because a comparison that limits Hebbia to uploaded data-room PDFs would understate its present positioning. Hebbia also shows collaborative projects and agent-driven finance tasks through Max.

This interface suits a pilot where coverage and exceptions must be visible across many items. The reviewer can inspect which rows are complete, which claims cite a source, and where a missing document blocks the result.

Public information does not show an itemized source package or dollar rate. Hebbia's pricing page routes buyers to a demo. Ask which filings, transcripts, market data, and licensed research arrive with the proposal and which depend on customer connections.

AllMind: connect the recurring process to the firm's own data

Our product thesis is that investment research depends on relationships across structured and unstructured information. AllMind licenses 6,800+ premium data sources from 100+ providers and partners, including S&P Global and Capital IQ data, FactSet data such as Revere, LSEG, MSCI, and exchange data such as CME. The corpus also covers estimates, filings, broker research, Expert Insights, and alternative data. Our ontology page connects that built-in data corpus with proprietary inputs, while Agent Studio uses both in configured workflows.

This architecture is relevant when a task must compare a new disclosure with a house model, prior thesis, portfolio context, or data warehouse. A recurring earnings note can use the same universe, permissions, calculation conventions, and output format each quarter. A supply-chain review can move between entities and documents without rebuilding the relationship manually for each query.

These are our own claims, not neutral results. We have no public dollar rate, we are not an order-entry, execution, or terminal-messaging system, and a use case that includes proprietary systems requires implementation. Our licensed corpus does include live market and exchange data. The ontology should be tested on the team's actual entity mapping, including aliases, subsidiaries, restatements, and missing data.

A reproducible three-way pilot using Salesforce filings

The old version of this article presented a Salesforce example as if product results had been observed. They had not. The more useful artifact is a public source packet and task specification any buyer can run.

Use Salesforce because its fiscal 2026 annual report and subsequent quarterly filing provide a compact test of changing guidance, segment information, acquisition references, and non-GAAP discussion. The source packet is public:

• Salesforce fiscal 2026 Form 10-K;
• Salesforce fiscal 2027 first-quarter Form 10-Q;
• the investor presentation and call transcript available through the issuer's investor relations site.

Add two internal artifacts that vendors cannot access before the test: the firm's prior Salesforce thesis and a small table of house estimates. Create two roles, one allowed to see both artifacts and one allowed to see only the public packet.

Ask each platform to produce the same one-page change note:

1. identify every change in full-year revenue and margin guidance between the annual and quarterly materials;
2. separate reported figures, management guidance, and the firm's own estimate;
3. explain the two largest drivers management names, with citations;
4. show where the internal thesis agrees or conflicts;
5. list unresolved questions and missing evidence;
6. return the result in the firm's approved headings and table format.

What to record

Do not combine the records into a single point total. A permission error should block deployment regardless of how polished the memo looks.

Match the result to the buyer

An investment team whose work starts with premium research may accept more output editing in exchange for AlphaSense's source breadth. A credit or deal team applying repeated questions across hundreds of documents may value Hebbia's visible coverage grid. A team whose work repeatedly joins market evidence with proprietary structured data may give more weight to AllMind's entity and workflow design.

Corporate strategy presents a different balance. Broad discovery, internal presentations, and external market material can make AlphaSense Enterprise Intelligence the most direct fit. A private-equity diligence group may narrow quickly to Hebbia if the data-room process dominates. A small public-equity desk that needs self-serve pricing may reject all three and evaluate narrower products.

What could not be verified

We could not verify common-task performance, contract-level content rights, comparable enterprise pricing, implementation hours, or product-specific Salesforce output. Public pages also do not show whether Hebbia or AllMind includes the exact broker publications available in a given AlphaSense contract. Our integration claims remain first-party.

The products have converged enough that category slogans are unreliable. Run the public packet, add the controlled internal artifacts, and require every vendor to return citations, permission evidence, exceptions, analyst repair time, and an itemized quote.

Comparison sources

• AlphaSense pricing, Expert Insights, Enterprise Intelligence, June 2026 product notes, and developer portal support its product description.
• Hebbia's product site and pricing page support Matrix, source-surface, and quote-model statements.
• AllMind descriptions are first-party from our ontology, data, and agent studio pages.
• The Salesforce 10-K and 10-Q are proposed test inputs, not evidence of any platform result.

If all three vendors are on your list, send them the same packet before the demo. Ask them to return the finished artifact and failure log without assistance, then review the evidence with the analysts and compliance team who will own the workflow.