Engine E1Classifiers
Accuracy parity
Is the model equally accurate for every customer group?
Compares accuracy across subgroups on your evaluation set and flags the gap.
Lynxor platform Built on AERIS
AI safety answers backed by tests, not opinions.
Lynxor turns the AERIS framework into a working safety assessment. It tests how your AI behaves on your own data and shows where it isn't safe yet, with every result mapped law by law. Built for the day anyone asks "is this safe?": your board, your customers or your regulator.
74
Fairness88%
Harm prevention71%
Transparency58%
Privacy83%
Accountability44%
212 trust controls in scope61 settled by tests9 gaps with ownersMapped to DPDP · RBI FREE-AI · CBUAE · PDPL · TRAI
350+ trust controls
5 deterministic engines
0 silent overwrites
How it works
Three steps from "is this AI safe?" to a signed-off scorecard.
01
Setup
Describe the system once: model types, customer journeys and the markets it runs in. Lynxor works out which trust controls apply and drops the rest.
Voicebot in the UAE: 212 of 347 controls apply
02
Answer
Control owners answer with evidence. Where a test can settle the question, an engine runs it on your data and proposes the answer.
Speech engine proposes: partially met
03
Results
A two-stage score rolls results up by Responsible AI pillar, then reports them as governance, reliability and compliance, law by law and market by market. The scorecard is saved, so the next run shows what changed.
Score 74, conditional go, 9 gaps with owners
Engines propose. People decide.
Try being the analyst.
An engine has tested the system and proposed an answer. Accept it, or override it with a reason. Either way, it goes on the record.
Proposed answer:
Validation engines
Five engines that test safety with evidence.
Each engine asks one plain question of your AI, runs the numbers on data you upload, and maps the result to the trust controls it can settle.
Engine E2Intent models
Intent stability
Does changing a name or dialect change what the bot thinks you want?
Swaps meaning-preserving details using a market-specific dictionary and counts intent flips.
Engine E3Speech
Speech fairness
Does the voicebot hear every accent equally well?
Measures word error rate by speaker group and flags the gap between best and worst.
Engine E-LLMLLMs
Response parity
Does the assistant refuse or decide differently for different people?
Compares paired responses for refusal rates and decision outcomes.
Engine E-DGDatasets
Dataset governance
Is the training data fit to build on?
Fourteen checks across fairness, privacy and quality, including PII exposure, proxy attributes and train/test leakage.
In developmentRoadmap
Explainability
Can you show why the model decided what it did?
Feature-level explanations for credit and risk models are in development.
Your models stay untouched. Engines work on evaluation data you upload. They never call your live systems, and the same input always gives the same result.
Too little data? Lynxor says so. Every engine drops groups below a minimum sample size and reports "insufficient data" instead of a misleading number.
Re-run after every change. New model, new prompt, new market. Run the assessment again and see exactly which scores moved.
See Lynxor run on one of your AI systems.
Bring a voicebot, a chatbot or a credit model. We'll scope it live and show you which controls an engine can settle on day one.
Scorecard figures and engine results shown on this page are illustrative.