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Executive & Clinical Diligence · 24 Hard Questions on Architecture, Safety Bounds & Evidence

Executive & Clinical Diligence.

We publish the proof, the standard, and the audit. Below are the definitive technical, clinical, and economic answers for healthcare systems, biopharma partners, regulators, and clinical investigators evaluating DeepSensi™.

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How is the SIL-4-class safety target quantified?
Safety architecture designed to a SIL-4-class target, quantified by the Company's own fault tree analysis under IEC 61025 across 23 independent barriers. No accredited third party has certified it. Under the open DeepSensi Standard (DSS-001 Platinum) and the Gomola Framework, clinical safety is formal mathematical verification rather than a prompt guardrail: the analysis models 23 deterministic verification barriers with a bounded common-cause failure factor. We publish the complete fault-tree derivation, its assumptions and the full figures at deepsensi.com/safety-architecture, so any institutional auditor can check the mathematics.
What is the architectural mechanism behind the missed-critical results?
The missed-critical results across 301 NEJM CPC cases are the direct result of the Gomola Framework’s dual-engine safety architecture: active multi-system cognitive debiasing paired with the LIMBO protocol. Standard generative models guess when facing ambiguity. DeepSensi is architected with a formal refusal to extrapolate: when evidentiary support falls below the Platinum threshold, the system declares clinical uncertainty, identifies the exact resolving biomarker or diagnostic study needed, and alerts senior clinical oversight. By eliminating the guessing inherent to commercial LLMs, DeepSensi converts diagnostic uncertainty into decisive clinical action.
How does DeepSensi compare clinically and economically to Big-Tech frontier models (OpenAI o3, GPT-5, Microsoft MAI-DxO, Fable)?
Frontier reasoning models (including OpenAI o3, GPT-5 class architectures, Fable, and Microsoft MAI-DxO) rely on multi-turn sequential exploratory prompting. In Microsoft’s published SDBench diagnostic benchmark (Nori et al. 2025, evaluated on the same NEJM CPC cases), reaching a diagnosis through frontier models required ordering thousands of dollars of sequential tests per case: OpenAI o3 averaged $7,850 in simulated tests, Microsoft MAI-DxO reached 85.5% at $7,184, and unaided physicians averaged $2,963. DeepSensi is engineered fundamentally differently: as the World’s Difficult Cases Engine, it synthesizes existing patient records directly in approximately 2 minutes and, when data is missing, names the single settling test rather than a costly shotgun battery.
How does DeepSensi eliminate data contamination and prove genuine pathophysiological deduction?
The anti-contamination barrier is architectural: every clinical record passes through an automated sanitization gate that strips bibliographic identifiers, author names, dates, institutional affiliations, and direct citations prior to ingestion. The diagnostic gap proves the mechanism: naked foundation model baselines evaluated on identical cases score only 39% to 49% Top-1, far below the DeepSensi consilium (the results are on our Science page). If memorization explained the result, raw models would not lag so far behind. Furthermore, input-grounding telemetry ensures every conclusion maps directly to documented patient signs, labs, and omics.
What is DeepSensi’s regulatory status with the US FDA and European authorities?
In the United States: FDA Pre-Submission (Q-Sub) submitted July 31, 2026 (FDA docket Q262487). DeepSensi is clinical decision support (CDS) software for licensed physicians, designed to meet the non-device criteria of section 520(o)(1)(E) of the FD&C Act. Regulatory status: deepsensi.com/regulatory. Across all jurisdictions, the treating physician retains full diagnostic authority, while liability and audit trails are cryptographically sealed.
How does the Inter-Specialty White Space Engine resolve multi-system medical odysseys?
Traditional medicine is fragmented into isolated clinical specialties. Over 80% of refractory and undiagnosed cases linger for years because the root cause lies in the metabolic, neuro-inflammatory, and toxicological interactions between organs: the “White Space”. DeepSensi’s engine integrates whole-genome sequencing (WGS) trio re-analysis, quantitative metabolomics (OAT 75+), microbiome metagenomics (HPHPA, 4-cresol), and compounding pharmacokinetics into an unified tensor, identifying systemic drivers that single-specialty consiliums overlook.
What is the global deployment footprint and infrastructure security?
DeepSensi is opening across four continents. For hospital networks and tertiary medical centers, DeepSensi deploys as a fully air-gapped on-premises Edge Node: complete neural inference, consensus reasoning, and the full safety barrier cascade execute locally without cloud connectivity, ensuring that zero protected health information (PHI) ever leaves the hospital. For decentralized clinical research and ambulatory practices, cryptographically secured cloud nodes provide zero-egress, privacy-preserving multi-omic consilium access.
How does the DeepSensi DCT™ paradigm advance clinical trials across the 9 disease pillars?
Spanning 9 disease domains, from drug-resistant epilepsy (EPI-RESOLVE) to neuroinflammation (NEURO-RESOLVE), autoimmunity (IMMUNO-RESOLVE), and supportive oncology (ONCO-SHIELD), DeepSensi DCT™ decentralizes clinical observational research. By equipping treating physicians and patients with precision cognitive tools at home, the platform accelerates real-world evidence (RWE) generation, eliminates traditional site recruitment delays, and drives clinical remission in patients without therapeutic alternatives.
Why is the DeepSensi Standard (DSS-001) published royalty-free?
A safety standard behind a commercial paywall is a product, not a standard. DeepSensi published the Gomola Framework and DSS-001 royalty-free so that hospitals, insurers, regulators, and competing AI developers can adopt, verify, and mandate rigorous reliability targets. We set a quantified safety bar and welcome all vendors to certify against it.
What is the Golden Horizon™ program and how does DeepSensi PBC fulfill its Public Benefit mandate?
As a Delaware Public Benefit Corporation, DeepSensi PBC legally writes clinical equity, open research, and public benefit patient access into its corporate charter.

The standard institutional value of a comprehensive multidisciplinary case evaluation and 12-month longitudinal tracking across our Precision Medicine Suites™ is $4,850. Through the Golden Horizon™ program, DeepSensi permanently allocates foundation compute and research subsidies to ensure up to 10,000 patients and families annually suffering from chronic, complex, or intractable diseases can access these elite capabilities at an immense discount ($790 protocol intake fee), with a dedicated 100% full hardship fee waiver pathway ($0 fee) for families with documented severe financial distress.

Under our Bring-Your-Own-Diagnostics (BYOD) model, DeepSensi provides cognitive multi-agent consilium software and deterministic SIL-4 decision support without selling or marking up lab tests; patients and treating clinicians provide their own required laboratory and omics panels performed at certified local facilities.

In parallel, Golden Horizon acts as an autonomous patient advocate: it scans global Phase II/III clinical trial registries and expanded-access (Right-to-Try) programs to connect refractory patients with cutting-edge experimental therapies at zero patient cost. As required under FDA and international bioethics guidelines, clinical research registry participation (RESOLVE™) remains 100% voluntary and free for all participants, completely independent of software purchases.

All figures are verified development-cohort results with confidence intervals published in the papers; a prospective confirmatory study (SILENT-DS) is pre-registered. Competitor benchmark figures: Nori et al. 2025 (arXiv:2506.22405), SDBench diagnostic protocol. We publish the proof, the standard, and the audit. We do not publish trade secrets or internal blueprints.

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