Physical-world intelligence
The world is the test.
Evaluate AI against the places, systems, and relationships that shape real decisions.
Evaluation Intelligence for the Physical World
AI meets the real world. Know how it performs.
Test spatial reasoning. Trace the evidence. Understand where AI succeeds—and where it needs to improve. Nexora brings benchmarks, expert evaluation, and curated data into one clear view.
Spatial
Infrastructure
Environment
Energy
Nexora in motion
Get the highlights.
GeoBench
Reasoning grounded in place.
Connect terrain, spatial evidence, and professional reasoning in a defined evaluation.
Evidence and evaluation
Every answer needs a foundation.
Inspect the evidence, assumptions, and constraints behind a recommendation.
01 / 03 Physical-world intelligence
One product family
From benchmark design to enterprise evaluation.
Bench, Eval, Data, and Enterprise connect the parts of a repeatable evaluation program. Engagements and deliverables are scoped to your use case.
Define the test
Nexora Bench
Standardized and private benchmarks for professional AI.
Typical scope
Benchmark specifications, reference expectations, versioned task sets, and documented coverage.
Evaluate the system
Nexora Eval
Custom evaluations for AI labs and enterprise teams.
Typical scope
Evaluation plans, scoring rubrics, failure analysis, and comparisons after model or workflow changes.
Ground the evidence
Nexora Data
Curated datasets, evaluation tasks, and scoring rubrics.
Typical scope
Dataset curation, source notes, provenance, coverage gaps, and agreed data-handling requirements.
Fit the organization
Nexora Enterprise
Private evaluation frameworks for your domain and workflows.
Typical scope
Scoped evaluation delivery, stakeholder review, benchmark maintenance, and ongoing support.
GeoBench / Nexora Bench
Intelligence. Grounded.
GeoBench evaluates professional geospatial reasoning across spatial analysis, GIS workflows, infrastructure, and the environment.
Model comparison
Model performance, in context.
Compare strengths across spatial accuracy, evidence grounding, reasoning, and reliability.
Model performance
| Model | |||||
|---|---|---|---|---|---|
| Model 1Evidence-grounded workflow | 88.0 | 92.0 | 84.0 | 90.0 | 88.6 |
| Model 2General-purpose baseline | 76.0 | 71.0 | 80.0 | 65.0 | 74.2 |
| Model 3Spatial tool-assisted workflow | 93.0 | 86.0 | 89.0 | 82.0 | 89.0 |
Illustrative scores and example tasks; not measured model results. Higher is better within this rubric.
Model order: 1, 2, 3.
Evaluation infrastructure
Measure. Understand. Improve.
Connect benchmark tasks, model outputs, and structured review in a repeatable evaluation workflow.
- 01
Evidence
Tasks · reference data · context
- 02
Evaluation
System outputs × task criteria
- 03
Scoring
Rubric · review · failure analysis
- 04
Intelligence
Findings · limitations · next steps
A traceable path from a real-world question to an evidence-based assessment.
How evaluation works
A clear path from question to findings.
Start with a focused evaluation, then repeat it when the model, evidence, or workflow changes.
- 01
Frame the decision
Define target tasks, systems, users, and the failure modes that matter.
- 02
Build the benchmark
Agree on reference evidence, scoring criteria, coverage, and data handling.
- 03
Evaluate and review
Compare outputs with the rubric, inspect failures, and document limitations.
- 04
Act on the findings
Deliver improvement priorities and scope repeat evaluations or benchmark maintenance.
Evaluation outputs
Findings your team can work with.
Evaluation plan & benchmark
A task specification, agreed reference dataset, and explicit scoring criteria.
Results & failure analysis
A scoped results summary with categorized errors, source context, and documented limitations.
Improvement priorities
Practical recommendations, with optional repeat evaluation and ongoing benchmark maintenance.
FAQ
Questions before you start
What can Nexora evaluate?
AI outputs and workflows involving spatial reasoning, location context, infrastructure, or enterprise decisions. Target tasks and supported systems are agreed during scoping.
How should I read benchmark results?
Read scores alongside task coverage, reference evidence, and the evaluation rubric. The comparison shown here uses illustrative scores; a measured evaluation includes the tested model configuration and supporting methodology.
What inputs are needed?
Start with the use case, target tasks, systems to evaluate, and available reference examples. Access and data requirements are agreed before evaluation begins.
How is private data handled?
Data access, permitted use, retention, and handling requirements must be agreed before private data is shared. Do not send sensitive datasets through the inquiry form.
What does an evaluation establish?
Findings apply to the agreed tasks, evidence, and system configuration. They do not establish certification, regulatory approval, or absolute safety. Repeat evaluations can be scoped after material changes.
Nexora AI
Start with a clearly defined need.
Tell us about your project. Scope and scheduling are confirmed after review.
A focused project. A practical path forward.