SystemsRoboticsAutonomy

AI agents for engineering complex physical systems

Investigate failures, reason across system evidence, and turn risks into traceable requirements and validation plans.

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Failure analysisSafety + securityValidationChange impact

Saphira / assurance graphLive system context

01 / sourceSystem model

02 / sourceField evidence

03 / sourceDesign change

Agent workflowInvestigate + reason3 evidence links active

decision 01Failure modes

decision 02Requirements

decision 03Validation plan

Customer evidence

Used on real machines, not hypothetical workflows

Published work across construction, humanoid, and industrial robotics.

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Published case study

Construction robotics

Raise Robotics designed certification into the robot

Safety questions became concrete architecture and component decisions before the design was expensive to change.

1 dayFirst risk assessment

~85Derived requirements

17–20 weeksCertification roadmap

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Published case study

Humanoid robotics

A shared safety language at 1X

Reviewable safety artifacts connected engineering, product, leadership, and external stakeholders.

“Saphira helped us turn that commitment into a shared language across engineering, product, and leadership.”

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Published case study

Industrial robotics

Faster safety engineering for RobCo

Requirements, analysis, and evidence were structured around the actual robot system for expert review.

“We highly value our partnership and the opportunities it presents for future certification challenges.”

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More teams building physical AI

Third-party validation

Saphira is a member of the NVIDIA Halos AI Systems Inspection Lab, and our safety engineers review generated analyses before they reach an assessor.

Where teams start

One hard decision at a time

Start with the workflow blocking a program today. Saphira reuses the same system context as the work expands.

Explore use cases

Failure and incident investigation

Trace symptoms across architecture, requirements, tests, logs, and field reports to identify likely causes and affected decisions.

RCA · FMEA · corrective action

Safety and security analysis

Investigate hazards and threats against the real system, with inspectable reasoning and source-linked expert review.

HARA · TARA · hazard analysis

Requirements and change impact

Turn findings into requirements and mitigations, then surface what a design or model change forces the team to reconsider.

Requirements · controls · traceability

Validation and test planning

Generate test and simulation plans from risk and requirements, connect results, and expose gaps before release.

V&V plans · simulation · coverage

The system underneath

A reusable AI system for expert engineering work

Different teams can begin with different problems because each workflow uses the same system context, controls, and evidence model.

Sources

Designs · requirements · tests · field data

System context

Linked, versioned, inspectable

Agent workflows

Investigate · reason · propose

Expert decisions

Review · approve · trace

Live assurance graphHuman review remains in control

Saphira traceability workspace connecting source documents, architecture, requirements, risk analysis, and validation tests

Cited

Reasoning retains sources and assumptions.

Controlled

Experts approve consequential analysis.

Continuous

Changes trigger impact across risks, tests, and evidence.

Works with your stack

Connect the sources of truth you already use

The team

Built by engineers who have shipped consequential systems

Saphira combines applied AI with reliability, systems engineering, functional safety, and enterprise software experience. The team has built and operated technology where failures have real-world consequences.

Start with the real system

Bring us the engineering decision your team cannot afford to get wrong

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