# SandboxAQ (CryptoSense) vs Unlearn.AI

SandboxAQ (CryptoSense) records 0 monthly visits with 0% month-over-month growth and Contact for Pricing pricing. Unlearn.AI records 17,107 monthly visits with -32.5% growth and Contact for Pricing pricing.

| Metric | [SandboxAQ (CryptoSense)](https://openaitools.app/tool/cryptosense-com) | [Unlearn.AI](https://openaitools.app/tool/unlearn-ai) |
| :--- | :--- | :--- |
| Website URL | https://cryptosense.com | https://unlearn.ai |
| Categories | AI Models, AI Healthcare, AI Medical Diagnosis, AI Research Tool | AI Healthcare, AI Research Tool, AI Predictions, AI Models |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 0 | 17,107 |
| MoM Growth Rate | 0% | -32.5% |
| Primary Market | Global | United States |
| Direct Visits | 0 | 7,493 |
| Review Score | 4.6 | 4.4 |
| Saved Count | 0 | 1 |
| Supported Platforms | Website | Website |

## SandboxAQ (CryptoSense)

SandboxAQ is an enterprise software company that develops Large Quantitative Models (LQMs): AI systems designed to answer scientific and security questions with quantitative, verifiable outputs rather than generated text. Instead of producing prose, LQMs combine physics simulations, chemistry knowledge and proprietary datasets to predict molecular behavior, material properties or cryptographic risk. The company frames this as "quantitative AI" — models that understand the physical world and support decisions in regulated, high-stakes industries.

The product portfolio is organized around verticals. In life sciences, BioSim LQMs and workflows support virtual screening, antibody design, lead optimization and toxicity prediction. In chemicals and materials, ChemSim models drive catalyst design, alloy development and solid-state battery modeling. In security, AQtive Guard delivers cryptographic discovery, inventory and post-quantum migration planning — technology that originated with CryptoSense, whose domain now redirects here. AQNav addresses GPS-denied navigation, and AQMed targets medical diagnostics such as cardiac triage.

SandboxAQ works with large enterprises, governments and research institutions, and has partnered with model providers such as Anthropic to expose LQM capabilities through familiar LLM interfaces. Because deployments are scoped to each organization's data and workflows, pricing is handled through a contact-sales process rather than published self-serve plans.

**Pricing & Plans:** Enterprise, contact-for-pricing model. SandboxAQ does not publish self-serve tiers; prospective customers request a demo and work with the sales team to scope deployments across LQMs, AQtive Guard, AQNav and AQMed.

- **Large Quantitative Models (LQMs):** AI models grounded in physics, chemistry and proprietary scientific data that return quantitative, no-hallucination outputs for real-world problems.
- **BioSim for drug discovery:** Workflows and scientific modules for virtual screening, antibody design, lead optimization, toxicity prediction and biomarker development.
- **ChemSim for materials science:** Catalyst design, alloy development, battery modeling and property prediction using DFT-based methods and generative AI.
- **AQtive Guard cybersecurity:** Cryptographic discovery, inventory and post-quantum migration planning, built on technology originally developed by CryptoSense.
- **AQNav and AQMed:** Quantum-sensing-based navigation for GPS-denied environments and AI-assisted medical diagnostics such as emergency cardiac triage.

**Strengths:** Backing and partnerships from major investors and collaborators, plus a landmark $500M CHIPS R&D award; Physics- and chemistry-grounded models designed to reduce hallucination in scientific workflows; Broad portfolio covering pharma, materials, cybersecurity, navigation and medical diagnostics; Interoperates with mainstream LLMs, letting teams access LQMs through familiar interfaces

**Limitations:** No published pricing or free trial; access requires a sales conversation; Enterprise and government focus means limited self-serve options for individuals or small teams; Several products are specialized and require domain expertise to deploy effectively

[Profile (Markdown)](https://openaitools.app/tool/cryptosense-com.md)

## Unlearn.AI

Unlearn.AI works with biopharma sponsors and clinical research teams at three points in a trial's lifecycle: before the protocol is finalized, while the study is running, and after readout. In design, teams model the patients they expect to enroll and pressure-test sample size, eligibility criteria, endpoints, and comparators before locking the protocol. The company's PROCOVA method — described as EMA-qualified and FDA-supported — applies twin-based covariate adjustment to reduce sample size in randomized controlled trials while holding statistical power, or to increase power without adding patients.

Mid-study, Unlearn supports pre-specified twin-based analyses that tighten interim estimates without enrolling anyone new, and lets teams re-run design scenarios when new evidence — a competitor readout, a fresh publication, or blinded data drifting from assumptions — changes the picture. After a study reads out, digital twins are used to contextualize population, endpoints, and variability, improving sensitivity across endpoints and helping teams re-evaluate historical trial data with regulatory-aligned methods.

Unlearn publishes concrete case outcomes: a 20% sample-size reduction built into a Phase 1/2 design with roughly $2M in savings, a 33% smaller control arm and four-plus months of enrollment time saved in a re-analysis of completed Phase 3 trials from a top-5 global sponsor, and a completed Phase 3 ALS trial re-analyzed to keep full power with 92 fewer participants. The company partners with sponsors including Trace Neuroscience, QurAlis, Acumen Pharmaceuticals, SOLA Biosciences, VectorY Therapeutics, and CHDI Foundation, and states it works with top global pharma companies. Unlearn does not publish list pricing; engagements are scoped through direct contact.

**Pricing & Plans:** Enterprise, contact-for-pricing model. Unlearn does not publish standard subscription tiers; engagements are scoped per study or program after a conversation with the team.

- **AI-generated patient digital twins:** Model-based forecasts of each participant's clinical outcomes, generated from baseline data already collected, that act as a virtual comparator or as additional information per enrolled patient.
- **PROCOVA design and analysis method:** A twin-based covariate adjustment approach that Unlearn describes as EMA-qualified and FDA-supported, used to reduce sample size in randomized controlled trials while maintaining power.
- **Trial design scenario modeling:** Build and compare design scenarios — endpoints, eligibility criteria, sample size, assumptions — so governance decisions are made with quantified tradeoffs instead of positions.
- **Interim and readout analysis support:** Pre-specified twin-based analyses added to the statistical analysis plan before unblinding, plus twin-adjusted analysis delivered at interim and at readout.
- **Externally controlled study design:** Design single-arm and externally controlled studies where randomization is not an option, selecting eligibility criteria that give open-label studies the best chance of a clear readout.

**Strengths:** Digital twins are generated from data already collected, so no extra patients are required to gain information; Publicly reported, quantified outcomes — sample-size reductions, cost savings, and enrollment time saved — rather than only qualitative claims; Covers the whole trial lifecycle: design, interim, and readout, not just one decision point; Named collaborations with biotech and pharma sponsors, plus regulatory-aligned methodology

**Limitations:** Pricing is not published, so budgeting requires a direct sales conversation; The value proposition depends on regulatory acceptance of twin-based methods, which varies by agency and indication; Best suited to sponsors with existing clinical data and statistical teams; not a self-serve tool; Public metrics come from specific studies and may not transfer directly to other indications

[Profile (Markdown)](https://openaitools.app/tool/unlearn-ai.md)

## Frequently Asked Questions

### How do SandboxAQ (CryptoSense) and Unlearn.AI compare in monthly traffic?

SandboxAQ (CryptoSense) has 0 monthly visits, while Unlearn.AI has 17,107. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

SandboxAQ (CryptoSense) has a month-over-month growth rate of 0%; Unlearn.AI has -32.5%.

### How does pricing compare?

SandboxAQ (CryptoSense): Contact for Pricing. Enterprise, contact-for-pricing model. SandboxAQ does not publish self-serve tiers; prospective customers request a demo and work with the sales team to scope deployments across LQMs, AQtive Guard, AQNav and AQMed. Unlearn.AI: Contact for Pricing. Enterprise, contact-for-pricing model. Unlearn does not publish standard subscription tiers; engagements are scoped per study or program after a conversation with the team.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Dr.Oracle](https://openaitools.app/tool/dr-oracle) | 723,847 | -1.2% | Free Trial | Guideline-directed medical answers with citations, built for clinicians | [.md](https://openaitools.app/tool/dr-oracle.md) |
| 2 | [Upheal](https://openaitools.app/tool/upheal-io) | 347,782 | -2.1% | Freemium, Free Trial, Contact for Pricing | AI therapy notes and an AI-native EHR built for mental health professionals. | [.md](https://openaitools.app/tool/upheal-io.md) |
| 3 | [Windward Maritime AI](https://openaitools.app/tool/windward-ai) | 128,777 | -38% | Contact for Pricing | All-source maritime intelligence powered by Maritime AI™ | [.md](https://openaitools.app/tool/windward-ai.md) |
| 4 | [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | -7.8% | Freemium, Usage-based | Build, simulate, evaluate and monitor self-improving AI agents | [.md](https://openaitools.app/tool/future-agi.md) |
| 5 | [SandboxAQ](https://openaitools.app/tool/sandboxaq) | 61,535 | +4.3% | Contact for Pricing | Large Quantitative Models that model the physical world for enterprise science and security | [.md](https://openaitools.app/tool/sandboxaq.md) |
| 6 | [Delve AI](https://openaitools.app/tool/delve) | 49,025 | -4.6% | Freemium | Generate data-backed personas, digital twins and synthetic research on demand | [.md](https://openaitools.app/tool/delve.md) |
| 7 | [Oatmeal Health](https://openaitools.app/tool/oatmeal-health) | 40,264 | -44.9% | Contact for Pricing | AI lung cancer screening that finds eligible patients and scores every nodule | [.md](https://openaitools.app/tool/oatmeal-health.md) |
| 8 | [RapidAI](https://openaitools.app/tool/rapidai-com) | 20,172 | -27.9% | Contact for Pricing | Enterprise clinical AI that turns medical imaging into faster, more confident care decisions. | [.md](https://openaitools.app/tool/rapidai-com.md) |
| 9 | [Cradle](https://openaitools.app/tool/cradle) | 16,993 | -22.3% | Contact for Pricing | Engineer better proteins, faster, with AI | [.md](https://openaitools.app/tool/cradle.md) |
| 10 | [Quantifind](https://openaitools.app/tool/quantifind-com) | 15,226 | +8.1% | Contact for Pricing | AI risk intelligence platform for financial crime and national security screening | [.md](https://openaitools.app/tool/quantifind-com.md) |

---
Source: https://openaitools.app/compare/cryptosense-com-vs-unlearn-ai

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)