Boosted.ai: 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.

Boosted.ai competes in buy-side research agents with Hebbia, Boosted.ai, LinqAlpha and Samaya AI, and with Reflexivity for quantitative and thematic work. Its white-label channel to brokerages and wealth platforms is unusual in our coverage.

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

• Domain tenure: Machine learning for investment managers since 2017; the CEO co-founded Maluuba, later acquired by Microsoft, and spent four years in ML at Bloomberg.
• Monitoring agents: Price, filing, news and earnings monitors with minute-level updates, plus email inbox monitoring.
• Channel breadth: Self-serve Individual and Pro tiers, institutional Prime, white-label Alfa Powered at Questrade and BX Partners, and an API with MCP.
• Certification claims: SOC 2 Type II and ISO 27001, 27701 and 42001 stated, with a no-training commitment.
• Cost-efficient AI: A fine-tuned capital-markets model that cut AI cost by 90% and can be deployed in the client's AWS VPC.
• Named clients: Liontrust, ChinaAMC, SS&C Eze and Questrade, with RBC as investor and client.

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 Boosted.ai. 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.

• Funding runway: No equity since a $15m bridge in November 2024, alongside growth capital from CIBC Innovation Banking; no open roles at our review.
• Traction figures: Client counts of 300+ and "180 of the largest asset managers" and AUM figures of $3trn and $5trn appear within months of each other; ARR is not disclosed.
• Performance evidence: The Alfa Prime result is an internal hypothetical backtest; no independent evaluation was located.
• Data provenance: Data licences are not disclosed, and broker and expert content were not located.
• Security paperwork: The security page says SOC 2 Type II audited and the trust centre says SOC 2 compliant; SSO/SCIM, zero data retention and MNPI controls were not located.
• Model supply chain: Model providers are not named.
• Office workflow: Native Excel, Office or Teams integration was not located.

Boosted.ai was founded in 2017 by Joshua Pantony, Jon Dorando and Nicholas Abe and is headquartered in Toronto and New York. Pantony co-founded Maluuba, a deep-learning company acquired by Microsoft, and spent four years as a principal ML engineer at Bloomberg. Christian Antaloczy, formerly of Meta, became CPO in 2025 and Jackie Wylie CMO in 2026.

Boosted.ai raised a US$8m Series A led by Portag3 in 2020 and a US$35m Series B led by Ten Coves and Spark in 2022, followed by C$10m of growth capital from CIBC Innovation Banking and a US$15m bridge in November 2024 with Fidelity Investments Canada funds, RBC and HarbourVest; the company reports US$61m of equity raised. It reports clients with more than $5trn in assets.

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

Boosted.ai presents a family of agent products for individuals, institutions and platforms. Figure 2 sets out our assessment of how it maps onto the investment workflow.

Alfa

Monitoring and research agents with citations, report redlining, document memory and inbox monitoring.

Alfa Prime

A multi-model investment committee with bull, bear and moderator agents producing a cited memo (August 2026, limited partners).

Alfa Powered

White-label agents for brokerages and wealth platforms, live at Questrade.

Platform API

A financial event bus, about 10,000 pre-built agents and MCP integration, priced by usage.

Scope of the product today

We did not locate Excel, Office or Teams integrations, broker research or expert-call content.

Boosted.ai moved from a closed-source model to a fine-tuned small capital-markets model in 2025, cutting AI cost by 90% and moving to minute-level updates; Alfa Prime uses several independent model families.

Evaluation evidence is internal: the Alfa Prime "twice the hit rate" claim comes from a hypothetical backtest. Model vendors are not named.

Boosted.ai draws on 150,000 sources including SEC filings, earnings calls, trade publications and local news, and ingests client documents, holdings, watchlists and email.

Licensed data vendors are not disclosed, coverage figures range from 15,000 to 60,000 securities, and broker and expert content were not located.

Boosted.ai fits monitoring and idea generation for asset and wealth managers: agents that watch positions and themes and report what changed.

It is not a modelling or Office deliverable tool.

Boosted.ai states SOC 2 Type II audited controls, ISO 27001, 27701 and 42001 certification, encryption, no training on client data and no cross-customer sharing, with hosting on AWS in Canada and the US. Figure 3 sets out what our public review located.

We would encourage buyers to request the SOC 2 report and ISO certificates, SSO and SCIM availability, model providers and retention terms.

The offsetting factor is unusual for its size: ISO 42001 for AI management alongside ISO 27001 and 27701.

Key Alfa features are free through self-serve sign-up, the API is usage-based with a free entry point, and Alfa Prime is bespoke.

The low entry cost makes evaluation easy. Buyers should confirm data sources before relying on outputs.

Boosted.ai sits in the Emerging quadrant of the GAII Intelligence Matrix, close to the investment-utility midpoint, held back by data disclosure and capital.

GAII initiation universe, 20 October 2026. Scores as currently published on the GAII Intelligence page; preliminary and based on public information.

11.1 Where Boosted.ai is strongest relative to peers

• Against research copilots, on persistent monitoring agents.
• Against enterprise-only vendors, on self-serve access and white-label distribution.
• Against peers at its stage, on the breadth of certification claims.

11.2 Areas of narrower functional focus

Boosted.ai has built depth in monitoring and distribution rather than content or desktop delivery.

• Content: no broker or expert research; data licences undisclosed.
• Desktop: no Office integration.
• Capital: no equity since 2024.
• Evidence: internal backtests only.

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 SOC 2 Type II report and ISO certificates.
• Availability of SSO/SAML and SCIM.
• Model providers and retention terms.
• Data vendors and licences.
• Runway and funding plans.
• Current headcount and hiring.
• Client count methodology.
• Named Alfa Prime partners.
• Alfa Prime evaluation methodology.
• Plans for Office integration.
• MNPI controls.
• Registered legal entity.

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 Boosted.ai specifically.

If you are a fundamental equity manager

• Use the free tier to test monitors on your positions.
• Ask which data sources sit behind outputs.
• Treat Alfa Prime backtests as indicative.

If you are responsible for technology or vendor risk

• Open with Figure 3; request certificates.
• Confirm SSO before institutional rollout.
• Assess VPC deployment of the model.

If you are assessing the category rather than the company

• Monitoring agents are a distinct buy-side need.
• White-label channels extend research AI to wealth platforms.
• Capital is concentrating among research AI vendors.

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