LLM Lucid AI
From generating to trusting: every answer verified, explained, and auditable.
Beyond output. Inside the reasoning.
Verified trust, not assumed
Triple-layer verification: automated claim-by-claim fact-checking, pairwise self-consistency, and a CoVe pipeline to reduce confirmation bias. Every claim gets a verdict, severity and evidence level.
Native explainability, not an add-on
Six complementary methods integrated into generation: token-level logprobs, prompt attribution, contrastive analysis, chain-of-thought, knowledge graphs and attention clustering.
Proactive risk detection
Hallucinations in 6 layers, 8 cognitive biases with EU AI Act relevance, and What-If testing with adversarial variants. Issues are detected before production.
Who benefits?
Data Scientists & ML Engineers
Debug outputs, evaluate quality, and compare models with objective metrics. See exactly where and when the model fails before production.
Audit, Compliance, Legal & Risk
Auditable explainability aligned with the EU AI Act. Full traceability per answer and session, exportable for audits without extra manual work.
Business teams
People who rely on outputs to act: managers, analysts, operators. They receive each LLM output with the confidence context they need.
From generation to analysis, in real time
Connect
Integrate via API. Compatible with major LLMs. SaaS or self-hosted.
Analyze
Every response runs through verification, bias detection, adversarial testing and scoring—while it is generated.
Document
Exportable evidence, per-answer traceability and audit-ready explainability for governance.
Use cases by industry
Financial advisory chatbots
The full loop: from query to trust
A customer asks a financial advisory chatbot about mortgages. Here is how LLM Lucid AI works end-to-end.
It combines a verifiable fact with a prediction: exactly where LLMs hallucinate or introduce bias without anyone noticing.
✓ Distinguishes factual vs. predictive intent
✓ Prepares independent verification
✓ Activates bias analysis on the answer
The team receives generated text. No confidence score. No claim verification. No idea if data is real, fabricated or outdated.
✗ “Experts expect”: no source, fabricated claim
✗ “Coming months”: unmarked temporal ambiguity
✗ Very different answer if rephrased
Claim verification
3.2% does not match current ECB sources. “Experts expect 2.8%” has no source: fabricated claim.
Three additional alerts
Low confidence on the exact number. Servility bias detected. Unstable across paraphrases.
Manual review before showing to customers
Regulatory-friendly explanation draft generated automatically. Exportable.
Illustrative example. Each deployment is adapted to your models, data and operations.
Built on our own R&D.
LLM Lucid AI incorporates in-house methodologies in explainability, semantic alignment, and probabilistic analysis for generative models, backed by formalized research lines currently in the publication process.
Two paths. One platform.
Whether you already have LLMs in production or you're just starting to evaluate them, AyGLOO adapts to you.
You already have LLMs deployed
AyGLOO connects as an observability layer on top of your existing models. No architecture changes, no production downtime.
- API integration with any provider
- SaaS or self-hosted depending on your requirements
- Use-case-specific configuration
- Minimal disruption, without touching the model
You're evaluating which LLM to use
AyGLOO helps you compare models, temperatures, and configurations with objective data before committing to a provider.
- Multi-model comparison with consistent metrics
- Testing at 3 temperatures per use case
- Objective quality score per dimension
- Informed decision, no vendor lock-in
Scope assessment
Review of use cases, current models, and compliance requirements. Pilot definition.
Pilot running
API integration, vertical-specific module setup, and first metrics on your own outputs.
Production launch
Full rollout, compliance documentation ready, and team training on the analysis dashboard.
Timelines are based on standard implementations for a single use case. Multi-use-case programs follow a phased roadmap.
Put a number on AI reliability.
A 45–60 minute workshop with Ops + IT to identify high-value use cases and define a concrete pilot.