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Transparent AI:
Explainability for LLM Outputs

Make every AI answer traceable, auditable and explainable building enterprise trust from the start. Explainability reduces bias, enables transparency and ensures compliance,
thereby making LLM adoption safer and faster

Transparent AI pipeline diagram showing prompts, retrieval, decoding, and validation layers for explainable LLM outputs
Transparent AI pipeline diagram showing prompts, retrieval, decoding, and validation layers for explainable LLM outputs
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Transparent AI pipeline diagram showing prompts, retrieval, decoding, and validation layers for explainable LLM outputs

Why Explainability Matters in LLMs?

LLMs power today’s copilots, search assistants and decision systems. But without explainability, trust erodes

Icon of magnifying glass representing traceability and explainability in AI outputs for enterprise LLM systems

Untraceable outputs

No way to verify correctness

Warning symbol highlighting hidden biases and compliance risks in large language model outputs

Hidden biases

Embedded risks from training data

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Operational Blind Spots

Hard to debug or scale AI pipelines

AI-Framework for External Explainability

Instead of reverse-engineering opaque model internals, Lymba focuses on the pipeline around the model

Document icon symbolizing input transparency with prompts, context, and knowledge retrieval in LLM explainability

What went in?

Prompts, injected system context and retrieved context

Slider control icon representing decoding parameters and alignment processes that make AI systems explainable

How it was processed?

Decoding parameters, filters and alignment roles

Citation link icon showing source attribution and output validation for trustworthy AI decisions

Where it came from?

Citations, validators and retrieval scores

Capabilities Built for Technical Leaders

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Input Transparency

See prompts, injected system context and conversation history side by side

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Source attribution

Citations with confidence scores and direct links back to original documents

Sliders icon representing decoding visibility for temperature, top-k, and confidence in AI model outputs

Decoding Visibility

Expose temperature, top-k/p sampling and token confidence scores

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Alignment Disclosure

Reveal fine-tuning datasets, system personas and safety guardrails

Checkmark badge icon representing validation, fact-checking, and conflict detection for reliable AI explainability

Validation & Post Processing

Automated fact-checking, conflict detection and confidence labeling

Speech bubble with question mark icon representing explainability UI features like Why This Answer, audit trails, and hover-to-explain

User-Facing Features

‘Why this answer?’ buttons, hover-to-explain tooltips, audit trails

From Explainability to Enterprise Trust

Explainability is not just technical detail - it’s business value

Infographic showing the flow from explainability to trust, adoption, and ROI in enterprise AI systems

Build confidence in AI copilots and assistants

Infographic showing the flow from explainability to trust, adoption, and ROI in enterprise AI systems

Strengthen compliance with audit trails and transparency

Infographic showing the flow from explainability to trust, adoption, and ROI in enterprise AI systems

Increase efficiency by reducing debugging and downtime

Infographic showing the flow from explainability to trust, adoption, and ROI in enterprise AI systems

Drive ROI with faster adoption and lower operational risk

  • Show both user and system input, making injections visible

  • Knowledge pulled from trusted sources with traceable citations and confidence scores

  • Adjustable temperature, top-k, and top-p let you balance precision vs. creativity

  • Consistency checks, schema enforcement and alternative completions ensure reliability

Build Trustworthy AI, From The Inside Out

Make every AI Decision Transparent, Traceable and Trustworthy by Design

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