Most "AI review" tools generate generic prose from a feedback form. Bynarize generates a structured narrative grounded in 12 months of signals (Goals, Feedback, Moments, Snapshots, Behavioural Indicators), runs it through guardrails (PII / Toxicity / Bias / Schema), fact-checks every claim against SignalAggregate, scores hallucination risk, calibrates confidence — and only THEN puts it in front of a manager. Section-by-section regenerate. Mandatory human edit. Full per-call trace. Manager edits feed back into the learning loop.
Most "AI for performance reviews" wraps a single LLM call around a feedback form. Bynarize wraps every AI call in a governed orchestrator with guardrails, RAG, fact-check, hallucination detection, calibrated confidence and a learning loop — so the output is fast, fair, accurate, calibrated, defensible AND the manager is always in control.
Single LLM call → "draft my review"
No grounding, no fact-check, no calibration, no audit — the manager either trusts blind or rewrites everything
Drafts grounded in 12 months of signals + Goals + Feedback + Moments, fact-checked vs SignalAggregate, hallucination-detected, confidence-calibrated, fully traced
"AI wrote nice things" — about a project the employee never touched
Reviews lose all credibility the first time a manager spots a hallucinated achievement
Hallucination Detector with severity-driven gating; fact-checker validates every claim vs source-of-truth before the manager sees it
Confidence shown as a flat "70%" with no calibration
Managers either over-trust or ignore the score — AI loses the room either way
AIConfidence aggregates model self-report + Platt/Isotonic calibration + coverage + recency + agreement; thresholds are tenant-configurable
Manager edits vanish into the void
Same prompt mistakes repeat next cycle; no learning happens
Manager edits feed AILearningEvent + AIFeedbackSignal; A/B prompt experiments with hourly lift/p-value pick the winners automatically
Generic feedback analysis: "this comment seems negative"
Themes, biases and recognition patterns stay invisible — managers still operate on gut feel
Per-feedback sentiment + theme + toxicity + bias-language analysis, employee theme aggregates, RecognitionGapAlert by severity with recommended action
No audit trail when an AI-written line decides a promotion
Legal, DEI and the employee all have valid grounds to challenge — and the company has no defence
Per-call AIExecutionRun + steps + RAG retrieval log + guardrail decisions + fact-check + confidence + cost — every AI decision defensible end-to-end
Managers stare at a blank review form three weekends in a row.
AI Review Narrative drafts strengths, improvements, behavioural insights, coaching and promotion language in seconds — manager edits with track-changes, original AI version always preserved.
"AI wrote nice things — but did the employee actually do any of them?"
Fact-checker extracts every claim and verifies it against SignalAggregate (PRs merged, tickets closed, kudos received, courses completed). Unverifiable claims are flagged BEFORE the manager sees them.
AI hallucinates a project the employee never worked on.
Hallucination Detector runs on every output with severity-driven Block / RequiresHumanReview / Allow. High-severity hallucinations never reach the manager — they go to a review queue with the original prompt and context.
"70% confidence" means nothing without calibration.
AIConfidence aggregates model self-report + Platt/Isotonic calibration + coverage + recency + agreement — so when we say 70% it really means 70%. Auto-publish vs human-review thresholds are configurable per task.
Feedback piles up but nobody knows what it actually says.
Per-feedback AI analysis on every entry — sentiment, toxicity, bias, themes — surfaced as chips on the feedback card and rolled up into employee theme aggregates.
Recognition goes to the loud people, never to the quiet ones.
EmployeeRecognitionAnalytics rolls 30/90/YTD recognition stats; RecognitionGapAlert flags systematically under-recognised employees by severity with a recommended next action.
Goals are created from gut feel — and three months later nobody can defend them.
AI Goal Recommender scans the employee's 12-month signal history, role skill requirements and team OKRs to pre-suggest goals before the form is opened. One click to accept; dismiss with reason feeds the learning loop.
"How did the AI come to that conclusion?" — silence.
Every AI output ships with a per-step trace: prompt version (signed hash), model + region, inputs, RAG documents retrieved, guardrail verdicts, fact-check results, confidence breakdown, cost and latency. Defensible to legal, DEI, audit and the employee.
Original AI version preserved forever. Manager edits captured with IsManagerEdited. Section-by-section regenerate. Acknowledgement workflow on the employee side.
Claims like "shipped 3 releases" are verified against SignalAggregate before the manager sees them. Unverifiable claims are flagged with a "needs review" chip.
AIConfidence aggregates model self-report + Platt/Isotonic calibration + coverage + recency + agreement. Auto-publish vs human-review thresholds are configurable per task.
Hallucination Detector runs on every output. High-severity hallucinations are blocked or routed to human review with full context — they never silently make it into a review.
50/50 traffic splits per task with hourly lift + p-value evaluation. Winning prompts auto-promoted; losers archived with full result history.
Per-call trace: prompt hash, model, region, inputs, RAG docs, guardrail verdicts, fact-check results, confidence breakdown, cost, latency — every AI decision is auditable end-to-end.
Bynarize AI Reviews accelerate the work without taking the judgement away — drafted, fact-checked, calibrated, audited and always edited by a human before it touches a career.