Scientific AI system evaluation
Authorized assessment of scientific language models, evaluation processes, and co-scientist agents within a client-approved scope.
Adversarial assurance for scientific AI systems.
Scientific language models, research agents, and sensitive data workflows are assessed through authorized, scoped evaluations and evidence-ready reporting.
Starting at $10,000 per engagement
Authorized assessment of scientific language models, evaluation processes, and co-scientist agents within a client-approved scope.
Review of biological, chemical, and other sensitive data workflows, including access boundaries, system controls, and handling expectations.
Documented findings, reproducible evaluation evidence, and prioritized recommendations for scientific, security, and compliance decision-makers.
Evaluate the stated behavior, reliability boundaries, and oversight controls of scientific language models and their test systems.
Confirm the system, intended use, permitted methods, and evaluation boundaries.
Agree on observable requirements, success measures, and evidence expectations.
Evaluate the system within the approved environment and scope.
Check observations against criteria and document relevant uncertainties.
Provide evidence and recommendations to the responsible system owners.
Applications
Scientific language models, model evaluation processes, and associated oversight systems.
Scope note
Evaluation is authorized and bounded. Findings describe the tested scope and do not guarantee future system behavior.
Contributing teams
Tortzal, Science
Review how research agents interact with tools, data, and human approval points within authorized systems.
Document intended tasks, connected tools, data, and human decision points.
Assess stated access controls, approvals, and operational responsibilities.
Conduct controlled checks within the authorized systems and methods.
Record results, evidence, limitations, and relevant workflow context.
Assign practical next steps to the teams responsible for the system.
Applications
Research agents, scientific toolchains, and human-in-the-loop workflows.
Scope note
Testing is limited to systems and actions explicitly authorized for the engagement.
Contributing teams
Tortzal, InfraOps, Ozcorp Scientific
Assess systems handling sensitive scientific datasets, including data movement, access, retention, and operational responsibility.
Identify data classes, systems, purposes, and agreed review boundaries.
Document relevant sources, transformations, storage, and access points.
Assess access, retention, validation, and operational ownership.
Describe observed control gaps and supporting context.
Prioritize improvements for the teams responsible for data handling.
Applications
Biological, chemical, and other sensitive scientific data workflows.
Scope note
The review does not replace the client’s legal, privacy, biosafety, or regulatory determinations.
Contributing teams
Tortzal, Science, InfraOps
Translate an approved evaluation into clear findings and next steps for technical, scientific, and governance teams.
Gather the approved scope, methods, observations, and evidence.
Check that conclusions are supported by recorded results.
Organize findings by significance, ownership, and remediation needs.
Communicate findings and limitations for technical and leadership readers.
Provide agreed records and a practical remediation roadmap.
Applications
Technical review, governance decisions, remediation planning, and risk communication.
Scope note
Reports reflect the tested scope, available evidence, and agreed evaluation criteria.
Contributing teams
Tortzal, Ozcorp Scientific
Work proceeds through scope definition, controlled evaluation, and reporting. Depending on the engagement, outputs may include an executive findings brief, scenario catalog, evidence package, and remediation roadmap.
Research organizations and teams developing or operating scientific AI systems, research agents, and sensitive scientific data workflows.
Ozcorp Scientific capability statement