Responsible AI Policy

FrootAI helps teams build AI agents they can trust. That starts with us being transparent about how our own platform works — what we do, what we don't do, and what we believe.

Last updated: May 2026 · Version 1.0

What FrootAI Does (and Doesn't Do)

We are an evaluation and quality assurance platform. We help teams measure whether their AI agents work correctly, safely, and reliably.

We do:

Score agent outputs for groundedness, safety, and quality
Provide deterministic, reproducible evaluation results
Integrate with CI/CD pipelines for automated testing
Surface regressions, anomalies, and failures
Help customers comply with AI Act evaluation requirements

We don't:

Train AI models
Make deployment decisions for customers
Use customer data to improve our own models
Censor or modify agent outputs
Guarantee that evaluated agents are safe (we measure; you decide)

Our Six Principles

Model Transparency

✓We proxy models — we don't train them. You choose which models power your agents.
✓For every model used in evaluation, we link to the provider's model card (capabilities, limitations, training data).
✓Rule-based checks can be deterministic; model-based scoring can vary and must be interpreted with its methodology and model context.
✓We document which eval primitives (groundedness, faithfulness, safety) use model-based scoring vs. rule-based scoring.
✓No black-box scoring — every metric has a published methodology.

Bias Mitigation

✓Our evaluation framework tests for bias explicitly: demographic fairness, language equity, cultural sensitivity.
✓We provide bias detection eval suites out of the box — customers can run them on their agents before production.
✓We acknowledge that model-based evaluation can inherit biases from the scoring model itself.
✓We intend to publish benchmark evidence only after the evaluated datasets, methods, and limitations are reviewable.
✓We encourage customers to include diverse test cases in their evaluation suites and provide templates to do so.

Human Oversight

✓FrootAI augments human judgment — it does not replace it. Evaluation scores inform decisions; humans make them.
✓Every automated eval suite can include human review checkpoints (e.g., 'flag for human review if safety score < 0.8').
✓We do not auto-deploy or auto-approve agents based on eval scores — that decision stays with the customer's team.
✓Deployment workflows should require human sign-off where the customer's risk assessment calls for it.
✓Our dashboards surface anomalies for human attention, not just aggregate scores.

Data Handling

✓FrootAI does not intentionally use customer account data to train a FrootAI model. Third-party model processing depends on the selected provider and contracted service terms.
✓Storage location, encryption, and tenant boundaries depend on the active service and deployment configuration; current public details are maintained in the Data Protection Notice.
✓Retention is service-specific. Current periods and account deletion behavior are documented in the Data Protection Notice.
✓GDPR data subject requests (export, deletion) are supported and documented.
✓Future enterprise return, deletion, and evidence terms apply only through a signed agreement.

Content Safety

✓We provide content safety evaluation primitives out of the box: toxicity, PII detection, jailbreak resistance, prompt injection detection.
✓Content safety guardrails are configurable per tenant — customers define their own thresholds based on their use case and risk tolerance.
✓We do not censor evaluation inputs or outputs — we score them. The decision to block or flag is the customer's.
✓Evidence immutability depends on the active storage and audit implementation; generated scores must not be treated as legal certification.
✓We document scoring methods when they are stable and reviewable.

Environmental Impact

✓We have not yet published a verified annual compute or energy footprint.
✓Infrastructure efficiency and regional sustainability claims require provider and workload evidence.
✓We optimize evaluation pipelines for efficiency: batch processing, caching, and deduplication reduce unnecessary compute.
✓We provide customers with per-evaluation cost and compute estimates so they can make informed decisions.
✓No carbon-neutrality target is represented as achieved or contractually committed on this page.

Governance

This policy is maintained by the FrootAI operator and reviewed when material product, legal, or operational changes occur.

Review cadence: At least annually once a formal governance program is active.
Trigger review: Major product changes, new regulations, or incidents.
Customer input: Contracted customers can raise concerns through their agreed contact path.
Public changes: Material updates are reflected by the page's last-updated date.

Questions?

If you have questions about this policy or FrootAI's approach to responsible AI, contact us at [email protected].