enSmaller sits between your data and any LLM — turning raw text into structured, verified context before a single token is processed.
AI models are only as good as the context they receive. enSmaller ensures that context is clean, relevant and complete — transforming how large language models perform in real business environments.
enSmaller analyses meaning, intent and relationships — not just text. Its proprietary linguistic engine builds human-level understanding into every interaction.
A transparent process that explains every decision and verifies each step, giving you trust, traceability and control.
Prevents waste, reduces hallucination and cuts token use — delivering measurable savings and enterprise-grade reliability.
Guides models to think logically — identifying contradictions, filling context gaps, and testing multiple hypotheses before asking the question of the LLM.
Unlike traditional RAG or agent orchestrators, enSmaller combines linguistic intelligence with deterministic governance. Every reasoning step is explainable, every decision traceable, and every outcome measured for accuracy and cost.
The result: enterprise-grade performance, transparency, and ROI from any LLM.
We're currently onboarding selected enterprise partners. If you're exploring AI optimisation, governance, or cost control, contact us to discuss early access.
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