At a glance
Aitros analysis is deterministic at the metric layer and evidence-grounded at the narrative layer. The system calculates core scores and trends using repeatable logic, then uses AI to enrich the analysis with themes, concerns, recommendations, and patterns. AI outputs are structured, validated, and tied back to real employee responses. Unsupported or malformed model output is filtered rather than trusted. The result is analysis that is clear, useful, and auditable back to real campaign data.
Trust & AI
How Aitros turns employee feedback into trusted analysis
Aitros uses AI to help organizations understand employee experience, leadership effectiveness, and culture more clearly. The analysis is not “AI making guesses.” The system is designed to keep the most important findings consistent, evidence-based, and reviewable.
Built on real response data
Aitros starts with actual campaign data: survey responses, assessment scores, session records, and employee conversation transcripts. Before model-based enrichment, the system organizes and cleans the data so analysis is grounded in real employee input—not free-form interpretation.
Reliable metrics first
Core metrics are calculated through structured, repeatable logic. Scores, distributions, trends, department breakdowns, engagement results, value scores, and leadership competency scores are computed algorithmically.
That means the numbers in your reports and dashboards are not invented by AI. They come from the same underlying data and can be reproduced consistently.
AI adds meaning, not made-up math
Aitros uses AI as an interpretation layer on top of verified data. The model helps identify themes, concerns, patterns, recommendations, and possible drivers behind the numbers.
That layer is constrained: outputs follow structured formats, insights connect back to real employee responses, and narrative findings are expected to include evidence where the workflow requires it.
Evidence-grounded insights
Major narrative insights are designed to be traceable back to campaign data. When Aitros identifies a concern, recommendation, theme, or pattern, it is tied to employee comments, survey questions, or other response evidence.
That makes the analysis easier to trust, review, and explain to stakeholders.
Unsupported AI output is filtered
Aitros is designed to reduce the risk of unsupported or low-quality AI claims. If a model-generated insight is malformed, unsupported, or missing required evidence, it is not treated as trusted analysis.
Instead of forcing a weak conclusion, the system drops the unsupported item or falls back to simpler analysis. Core quantitative results stay reliable even when an enrichment layer is incomplete.
Always fresh and versioned
Analysis artifacts are versioned and checked for freshness. When new responses arrive, existing analysis can be marked stale so the system knows when reanalysis is needed.
That helps prevent outdated summaries or recommendations from being shown as current.
Privacy and trust guardrails
Aitros includes safeguards for confidentiality. Analysis can respect minimum response thresholds, and sparse or overly identifiable segments can be suppressed.
That protects employees and reduces the risk of over-interpreting very small samples.
Built for auditability
The goal is not only attractive dashboards. The goal is analysis leaders can rely on when making decisions.
Aitros combines deterministic metrics, structured AI enrichment, evidence-linking, freshness checks, and privacy guardrails so organizations can move from raw feedback to clear, credible action.
Trust statement
We do not claim that AI eliminates all risk of error. Aitros is designed to minimize and contain hallucination risk through strict validation, evidence-linking, rejection of unsupported outputs, deterministic fallbacks, privacy thresholds, and auditability. Leaders should still apply judgment—especially on sampling, non-response, and organizational context.