# BullFrog AI Holdings, Inc.

> Clarifo company profile — qualitative business description generated from
> the company's filings. Financial statements, charts and ratios are
> available on Clarifo (https://www.clarifo.com/en/companies/BullFrog AI Holdings, Inc.).

## Overview

BullFrog AI Holdings, Inc. is a U.S.-based AI/ML company focused on applying machine learning to complex biomedical data to support drug discovery and development. The company’s core platform, bfLEAP™, originated from technology developed at The Johns Hopkins University Applied Physics Laboratory and has since been expanded with additional models and tools. BullFrog AI combines software-driven analytics services with a broader strategy of partnering with biopharma companies and, longer term, acquiring drug rights and using its platform to accelerate development. The business is still early-stage and pre-commercial at scale, with limited revenue history and a continuing need for external financing to fund operations.

## Products & services

• bfLEAP™ AI/ML platform for biomedical data analysis
• Drug target discovery and analytical services
• Biopharma collaboration and workflow integration services
• Preclinical development support for in-licensed drug programs
• Proprietary AI/ML tools and custom scripts for data analysis

- **AI/ML biomedical analytics platform** (40%) — bfLEAP™ and related machine-learning tools used to analyze complex medical and life-science datasets.
- **Biopharma analytical services** (35%) — Custom analysis engagements for drug discovery, target identification, and workflow integration.
- **Strategic collaborations** (15%) — Partnerships with biopharma and research organizations structured around fees, equity, or IP.
- **In-licensed drug programs** (10%) — Acquired or licensed drug candidates advanced using the company’s AI/ML capabilities.

- bfLEAP™ AI/ML platform for biomedical data analysis
- Drug target discovery and analytical services
- Biopharma collaboration and workflow integration services
- Preclinical development support for in-licensed drug programs
- Proprietary AI/ML tools and custom scripts for data analysis

## Customers

BullFrog AI sells primarily to biopharma companies that want to reduce the time, cost, and risk of drug development by using AI/ML to interrogate complex datasets. Its collaboration model suggests customers may pay in cash, equity, or other consideration depending on the scope and strategic value of the engagement. The company also works with research-oriented partners and public or proprietary health-data sources to improve model performance and expand its analytical capabilities. In the longer term, the company’s drug-asset strategy implies it may also create value through partnerships around acquired or in-licensed programs rather than only through pure software sales.

- **Biopharma collaborators** (primary) — Drug developers that buy analytical services and platform access to reduce development risk and speed decision-making.
- **Clinical and preclinical drug programs** (secondary) — Partners around in-licensed or acquired drug assets that need AI/ML support to advance candidates toward value-creating milestones.
- **Research institutions and data partners** (secondary) — Organizations that provide datasets, validation, or scientific collaboration to strengthen the platform and expand use cases.
- **Public health and proprietary data holders** (emerging) — Data owners that enable new discovery workflows and improve model training and analytical depth.

- Biopharma companies seeking AI-driven drug discovery insights
- Oncology and rare-disease developers needing faster target analysis
- Research collaborators that contribute data, expertise, or validation
- Partners that prefer flexible deal structures, including equity or IP
- Organizations with large health datasets that need custom analytics

## Geography

BullFrog AI is headquartered in the United States and its operations are currently conducted through its U.S. corporate entities. The company’s reported business activity is centered on domestic R&D, collaboration development, and customer engagement rather than a broad international operating footprint. Because the company is early-stage, geography matters mainly through access to U.S. capital markets, U.S.-based scientific partners, and U.S. health-data sources. No country-level revenue concentration was disclosed in the provided excerpts, so the geographic profile should be viewed as operationally U.S.-centric with limited disclosed international exposure.

- Headquartered and operated in the United States
- U.S. capital markets are important for funding and liquidity
- Scientific collaboration and data access are primarily U.S.-based
- No disclosed country-level revenue mix in the provided excerpts
- Geographic exposure is driven more by partner location than by sales footprint

## Strategy

BullFrog AI’s near-term strategy is to generate revenue through strategic relationships with biopharma companies, using its AI/ML platform to deliver insights that reduce drug-development risk and accelerate workflows. The company is willing to structure engagements flexibly, including cash, equity, or IP-based consideration, which can help win early partnerships but also adds variability to revenue quality. A second strategic pillar is to acquire rights to drug assets at different development stages and use bfLEAP™ to create near-term value before monetizing the programs, ideally within about 30 months. This strategy is designed to combine software-like analytics revenue with higher-upside asset development, but it requires capital, execution discipline, and scientific validation.

- **Expand biopharma collaborations** (short-term) — Partnership revenue is the company’s main near-term commercialization path and helps validate the platform.
- **Advance bfLEAP™ capabilities** (medium-term) — Improving the AI/ML platform increases the quality of insights and strengthens differentiation in drug discovery analytics.
- **Acquire and develop drug assets** (medium-term) — Owning or licensing programs can create higher-value monetization opportunities than services alone.

- Build revenue through biopharma partnerships and analytical services
- Use flexible deal structures to match partner needs and preserve upside
- Apply bfLEAP™ to reduce drug-development risk and speed decisions
- Acquire or license drug assets and advance them with AI/ML support
- Monetize programs quickly after value creation, targeting short development cycles

## Risks

BullFrog AI faces substantial going-concern and financing risk because it has limited recurring revenue and has stated that current cash is not sufficient to fund planned operations for at least a year. The company also disclosed unremediated material weaknesses in internal control over financial reporting, which increases the risk of reporting errors and can complicate access to capital. Commercial execution risk is high because the business depends on winning a small number of strategic biopharma collaborations and proving that its AI/ML outputs create measurable value. More broadly, AI-enabled drug discovery is a competitive and scientifically uncertain market, so the company faces the risk that models do not translate into successful development outcomes, partner demand remains limited, or funding conditions tighten.

- **Going-concern and funding shortfall** [critical] — The company disclosed that cash is not sufficient to fund planned operations for at least a year and it has no committed future funding.
- **Internal control material weakness** [high] — Management reported unremediated material weaknesses in financial reporting controls, increasing the risk of misstatement and delayed reporting.
- **Customer and partnership concentration** [high] — Revenue depends on a limited number of biopharma collaborations and the company has only a short commercial history.
- **AI/ML model and drug-development uncertainty** [high] — The platform must prove that its analytics improve drug-development outcomes, which is inherently uncertain and data-dependent.

- Going-concern and liquidity risk due to insufficient cash for planned operations
- Dependence on external financing, including equity sales and ATM usage
- Unremediated material weaknesses in internal control over financial reporting
- Revenue concentration risk from a small number of collaborations
- Scientific and commercial uncertainty in AI-driven drug discovery
- Execution risk in acquiring and monetizing drug assets within short timeframes

## Accounting

BullFrog AI’s accounting profile is shaped by its early-stage status, limited revenue history, and reliance on estimates and judgments under U.S. GAAP. Revenue recognition is important because the company has reported only small, episodic service revenue and collaboration arrangements may involve cash, equity, or other non-cash consideration, which can complicate measurement and timing. The company also disclosed that it has not remediated material weaknesses in internal controls, so investors should place extra attention on the reliability of reported balances, expense classification, and disclosure completeness. As a development-stage company, operating results can fluctuate materially from quarter to quarter based on contract timing, R&D spending, and financing-related costs, making period-to-period comparisons less stable than in mature software or biotech businesses.

- **Revenue recognition for collaborations and services** — Can shift revenue between periods and affect comparability
- **Fair value measurement of non-cash consideration** — Can materially affect reported revenue and other income
- **Internal control remediation** — Affects confidence in all reported financial statement line items
- **Going-concern assessment** — Influences investor assessment of solvency and dilution risk

- Revenue recognition for collaboration and service contracts can be timing-sensitive
- Non-cash consideration such as equity or IP may require valuation judgment
- R&D and preclinical spending can cause large quarterly expense swings
- Material weaknesses increase the risk of misstatement or delayed detection
- Going-concern disclosures and financing costs are important to interpret reported results

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*Last updated: 2026-08-11T04:46:25.061677+00:00*