Tensalis is designed as an independent verification layer for AI systems that generate natural-language responses from retrieved or provided context. Its primary goal is to assess whether generated outputs are logically supported by the reference information supplied to the model.
Modern RAG and LLM-based systems typically evaluate outputs using a combination of retrieval relevance, semantic similarity, or LLM-as-a-judge techniques. Frameworks such as RAGAS, TruLens, and similar tools have helped standardize this space by making evaluation more systematic and measurable.
However, similarity-oriented scoring primarily reflects topical alignment and linguistic overlap. In practice, this means that responses may appear highly relevant to a source while still differing on specific factual elements such as quantities, conditions, dates, or policy constraints.
Tensalis is designed to complement these approaches by focusing explicitly on logical consistency between generated statements and reference material.
At a conceptual level, Tensalis applies Natural Language Inference (NLI) techniques to evaluate the relationship between generated output and reference facts. Rather than measuring similarity, NLI models assess whether a statement is:
This framing allows Tensalis to reason about factual alignment at the level of claims and constraints, which is particularly important for enterprise use cases involving policies, pricing, compliance rules, or contractual language.
From an architectural perspective, Tensalis operates as a stateless verification service that can be invoked after an AI system produces a candidate response.
Application / Agent
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│ Generated response + reference context
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Tensalis Verification Layer
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│ Confidence-based assessment
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Application Decision Logic
The verification result can then be used by the calling system to accept the response, request regeneration, flag uncertainty, or apply additional business logic.
| Principle | Rationale |
|---|---|
| Model-agnostic integration | Designed to work alongside different LLM providers and retrieval strategies |
| Low operational overhead | Optimized for production workflows without requiring LLM-based evaluation calls |
| Clear decision semantics | Returns a concise verification signal suitable for automated pipelines |
| Enterprise alignment | Focused on use cases where factual correctness and auditability matter |
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