AlphaSense: Initiation of Coverage

GAII Intelligence defines the AI Research & Intelligence market as software that applies generative and agentic AI to the research process of professional investors: locating candidate securities, assembling and interrogating evidence, producing written analysis with traceable sources, and monitoring positions once held. Buyers are hedge funds, long-only managers, multi-manager platforms, family offices and allocators; the economic buyer is typically the chief technology officer, the head of research, or a portfolio manager with a technology budget.

Capabilities we treat as mandatory

• Evidence retrieval at scale: the ability to search and reason across regulatory filings, transcripts and news, and across documents the client supplies.
• Source traceability: every material claim in an output must be reachable back to the passage that supports it.
• Institutional data handling: a stated position on where client data goes, how long it is held, and whether it is used for model training.

Capabilities we treat as differentiating

• Persistent state: the system remembers a thesis, a coverage list or a position between sessions and acts on it without being re-prompted.
• Workflow surfaces: delivery inside the applications analysts already use — the spreadsheet, the research management system, email and messaging.
• Owned or exclusive content: material that a competitor cannot obtain by signing the same third-party contract.
• Published evaluation: a measured, disclosed accuracy methodology rather than an assertion of quality.

AlphaSense is the reference platform for market intelligence. Hebbia and Rogo compete for the agentic research layer but largely connect to content the client already licenses; Bloomberg and FactSet are the incumbent alternatives for content, and Third Bridge and AlphaSights for expert calls.

The following are the capabilities and characteristics that, in our assessment, most distinguish AlphaSense within the AI Research & Intelligence market. Each is drawn from publicly documented product capability, published interviews with management, or independent hands-on review.

• Content no peer combines: Over 500 million documents, 1,500+ broker sources, about 300,000 expert transcripts, Canalyst models and data on 10 million private companies, in one platform.
• Scale and capital: ARR above $700m in the second quarter of 2026, about 8,000 customers and a $7.5bn valuation; vendor-continuity risk is the lowest in our coverage alongside Rogo.
• Analyst validation: Named a Leader in Gartner's inaugural Magic Quadrant for competitive and market intelligence and the only Leader in the Forrester Wave for the third quarter of 2026.
• Expert-call compliance: Affirmative compliance approval of transcripts, expert screening, forensic audits and a client Compliance Portal, overseen by a Chief Compliance Officer.
• Security posture: SOC 2 Type 2, ISO/IEC 27001:2022, SAML SSO, SCIM, customer-managed keys, an EU data region and a stated zero-training, zero-retention policy.
• Agent roadmap: Deep Research, workflow and thesis agents, and SuperAnalyst with reusable Skills, shipped on a regular cadence.
• Penetration across the buy side and sell side: About 90% of top asset managers and the top investment banks, with more than 100 banks using its agents.

The following are factors that prospective clients should weigh, and areas where our public review did not find sufficient evidence to reach a settled view. Several are common across vendors at this stage of the market and are not specific to AlphaSense. We encourage prospective clients to raise each of them directly with the company; we have done the same, and will update this note as answers arrive.

• Pricing: No list prices are published; seat figures are third-party estimates, and a reported move toward consumption pricing has not been confirmed by the company. Buyers should model agent and credit usage and seek renewal caps.
• Model providers: Current model providers are not named, and the trust-centre subprocessor list does not include the cloud and model infrastructure named on the security page, such as AWS Bedrock. We encourage buyers to request providers, regions and contractual zero-retention terms.
• Product maturity: SuperAnalyst launched in October 2026 and the PowerPoint and Excel assistants remain in open beta. Buyers should pilot on their own workflows.
• Data residency: The EU region applies to Enterprise Intelligence clients, with some login, billing and integration data possibly outside the EU; a dedicated private cloud is described in marketing only.
• Expert-content controls: Instant Transcripts give call-takers unreviewed transcripts; buyers should confirm how this fits their compliance policy, including for AI-led interviews.
• Content embargoes: Real-time broker research requires each broker's entitlement, and some research arrives up to seven days later. Buyers should confirm what their user types will see.
• Ownership change: The company has said public markets are a natural path. Buyers may wish to negotiate price protection and change-of-control terms.
• Evaluation evidence: No technical benchmark or independent accuracy test has been published.

AlphaSense, Inc. was launched in 2011 by Jack Kokko, a former Morgan Stanley analyst, and Raj Neervannan, and is headquartered at Hudson Yards in New York, with offices in London, Helsinki, Germany, India and Singapore. It acquired Stream (2021), Sentieo (2022), Tegus for $930m (2024) — bringing BamSEC and Canalyst — and Carousel (2025).

AlphaSense raised $225m at $1.7bn in 2022, $150m at $2.5bn in 2023, $650m at $4bn in 2024 and $350m at $7.5bn in June 2026, co-led by Vitruvian, Accenture Ventures and J.P. Morgan Asset Management; the company says it has raised well over $1bn. ARR rose from about $100m in 2022 to over $700m in the second quarter of 2026. Samantha Greenberg became CFO in April 2026.

Company-reported figures are shown as such and are not independently verified. Verification is carried into the vendor questionnaire.

AlphaSense presents a single platform spanning search, generative research, agents, expert insights and financial data. Figure 2 sets out our assessment of how it maps onto the investment workflow.

Search and Generative Search

Search and AI answers across licensed and client content with sentence-level citations; a next-generation agentic version followed in 2026.

Deep Research and agents

Multi-step research, workflow agents (IPO primer, bull and bear debate, channel checks) and thesis agents.

SuperAnalyst

An always-on agent with a Context Graph, Skills and a Skill builder, launched in October 2026 after early access from June.

Expert Insights and Financial Data

Expert transcripts, AI Interviewer calls, Canalyst models and an Excel formulas add-in.

Scope of the product today

We did not locate a production MCP server — the company documents a sample, self-hosted wrapper — or Microsoft Teams, Copilot, Claude or ChatGPT connectors.

AlphaSense built task-specific models fine-tuned from open-source bases and a hybrid document and vector search architecture, and runs Llama models on Cerebras hardware with zero data retention. Its Context Graph links each data point to its source, and outputs carry sentence-level citations.

The company does not name the frontier model providers behind current products and has not published a technical benchmark; its analyst placements evaluate the platform, not model accuracy.

Content spans more than 500 million documents in 37+ languages: broker research from 1,500+ providers, about 300,000 expert transcripts, filings, live transcripts for 5,000+ companies, news and trade journals, Canalyst models and data on 10 million private companies, with EIU research added in October 2026.

Broker research is subject to each broker's entitlement and embargo rules. Client content is searched alongside external content through Enterprise Intelligence, with Dropbox, Salesforce and Snowflake connectors.

AlphaSense fits the research cycle from idea to monitoring: search and channel checks for ideas, expert calls and broker research for diligence, and thesis agents for validation and post-mortems.

Modelling and Office delivery are newer. Canalyst models and the Carousel acquisition strengthen Excel work, and the Office add-ins were in beta in mid-2026.

AlphaSense holds SOC 2 Type 2 and ISO/IEC 27001:2022, publishes a SafeBase trust centre, and supports SAML SSO, SCIM, customer-managed keys and an EU region, with a stated zero-training and zero-retention policy. Figure 3 sets out what our public review located.

We would encourage buyers to request current model providers and regions, a complete subprocessor list, retention periods, admin audit-log capability and confirmation of private-cloud options.

The offsetting factors are substantial. The expert-call compliance programme is the most developed in our coverage, and the company scored highest on security in the Forrester Wave.

AlphaSense does not publish prices. Third parties estimate $10,000–20,000 per seat per year, with enterprise contracts from about $50,000 to over $1m; expert calls are charged at the expert's rate plus a transcription fee, and API use is metered in credits.

The value case is consolidation: one platform replacing several content and expert subscriptions. Buyers should model usage-based costs as agents take on more work.

AlphaSense sits in the Leaders quadrant of the GAII Intelligence Matrix with the highest investment utility we have measured, driven by content. It is the highest-rated company in our coverage.

GAII coverage universe, 21 October 2026. Scores as currently published on the GAII Intelligence page; preliminary and based on public information.

11.1 Where AlphaSense is strongest relative to peers

• Against AI-native research platforms, on owned and licensed content they cannot supply.
• Against content incumbents, on the agent layer built over that content.
• Against expert networks, on a searchable transcript library and AI-led interviews.

11.2 Areas of narrower functional focus

AlphaSense has built depth in content and search rather than in deliverables or open agent access.

• Deliverables: Office assistants in beta in mid-2026.
• Developer access: no production MCP server located.
• Commercial: pricing opaque and changing.
• Disclosure: model providers not named.

Scores are on a 0–10 scale. Each dimension score is the mean of its sub-criteria, and the GAII Overall Score is the weighted mean of the six dimensions. The detailed sub-criteria scores that build each dimension are set out in Appendix A.

These are the areas where our public review did not find sufficient evidence to form a settled view. We have put them to the company and will update this note when they are answered. Prospective clients may find them a useful agenda for their own evaluation call.

• The model providers and regions used for current products.
• A complete subprocessor list including cloud and model providers.
• Retention periods for customer data and admin audit-log capability.
• Whether a dedicated private-cloud deployment is available.
• General-availability dates for SuperAnalyst and the Office add-ins.
• Pricing for seats, agents and credits, and the planned packaging changes.
• How Instant Transcripts fit the transcript review process.
• MNPI controls for AI-led expert interviews.
• The equity total raised.
• Current headcount.
• Plans for a production MCP server.
• Independent evaluations of accuracy.

This note is an assessment of a vendor against a defined framework, not a recommendation to buy or to decline. The following are the considerations we would put at the centre of an evaluation of AlphaSense specifically.

If you are a fundamental equity manager

• Test Deep Research and thesis agents on a live position.
• Check which broker research your user types see in real time.
• Model agent usage before renewal.

If you are responsible for technology or vendor risk

• Open with Figure 3; request providers and the full subprocessor list.
• Confirm residency for all data, not only Enterprise Intelligence.
• Pilot the Office add-ins before relying on them.

If you are assessing the category rather than the company

• Content ownership is a durable advantage as agents commoditise reasoning.
• Incumbents and AI-native vendors are converging on agent platforms.
• Consumption pricing is spreading; budgets need usage forecasts.

Each dimension score in Section 12 is the mean of the sub-criteria below.