Independent AI Evaluation

Independent AI evaluation for science, education, and evidence-based systems.

JNO Systems tests AI tools and evidence workflows for factual accuracy, grounding, transparency, usability, and real-world failure risk before people rely on them.

Request a Scope Review View Sample Audit Work
What JNO Systems Evaluates

Evidence quality across tools, workflows, and real-world records.

JNO Systems evaluates complex systems where evidence quality matters, from AI tools to field observations.

Science AI Audits

Independent evaluation of EdTech tools, classroom AI, curriculum platforms, and AI-generated instructional content.

AI Reliability Reviews

Testing for hallucination risk, false grounding, citation failure, correction behavior, and system-level breakdowns.

Field Research Systems

Structured observation, multimodal documentation, metadata discipline, and evidence-quality workflows for reviewable field records.

Media Systems & Multimodal QA

Review of video, audio, subtitles, metadata, localization files, and cross-modal consistency for production, delivery, and AI evaluation workflows.

Field Research

Field Research & Evidence Systems

Field research at JNO Systems supports evidence collection, structured observation, and documentation workflows used in evaluation, audit, and operational review contexts.

This work includes wildlife observation records, photo and video evidence capture, field notes, date/time context, metadata discipline, repeatable field protocols, and reviewable records. The same principles used in AI evaluation apply in the field: clear observations, controlled claims, reliable records, and evidence that can be reviewed later.

Wildlife Observation

Hummingbirds, monarch butterflies, pollinators, urban birds, and backyard ecological systems.

Multimodal Documentation

Photo, video, field notes, date/time records, location context, and behavioral observations.

Evidence Quality

Structured logs, repeatable protocols, metadata consistency, and reviewable records.

Proof / Sample Audit Work

Sample AI audit work.

Examples are summarized here for first-read clarity. Detailed methodology, scoring, and deliverable language are intentionally kept off the homepage surface.

NotebookLM / False grounding

Source-looking answers without source support.

The tool produced confident answers that appeared grounded even when source material did not support the output. The failure was not obvious in normal review.

MagicSchool AI / Science accuracy

Errors persisted under correction.

AP Biology responses included exam-level science errors and similar mistakes continued after correction, creating classroom reliability risk.

Wayground / Layer divergence

Standards metadata diverged from content.

The tool generated credible biology content while attaching unsupported NGSS performance expectation codes, creating implementation and reporting risk.

Methodology Summary

Structured review, controlled claims, reviewable evidence.

JNO Systems uses scoped reviews instead of fixed service tiers because each evaluation depends on the product, audience, risk level, available evidence, and intended use.

  • Evaluate factual accuracy, grounding, transparency, usability, and system behavior.
  • Use structured prompts, documented observations, and reviewable evidence records.
  • Keep claims controlled and recommendations tied to observed failure risk.
Scope Review

Request a Scope Review

Share the product, workflow, or evaluation question you want reviewed. JNO Systems will assess fit, scope, required evidence, and the appropriate review approach before work begins.

Request a Scope Review
Email
jon@jnosystems.com
Location
Granada Hills, CA
Entity
JNO Systems LLC