Generic chatbots free-associate from public training data. EnhanceLLM answers only from the documents your organization has authorized — internal policies, product specs, support tickets, contracts — and attaches a citation to every claim so the answer can be audited.
The platform combines hybrid retrieval, cross-encoder re-ranking, context engineering, and a verification step that blocks unsupported statements before they reach the user. Every request is scoped to what that specific user is allowed to see, enforced before retrieval rather than after generation.
A single coherent flow — retrieval, re-ranking, context engineering, grounded generation, verification, and governance — designed for organizations that need trustworthy answers from their own knowledge.
Every answer comes with inline citations to the source document.
The assistant answers only from retrieved, authorized evidence. A verification step checks each claim against its cited source and blocks unsupported statements before they reach the user.
Answers are scoped to what each individual user is allowed to see.
Access rights are enforced before retrieval, not after generation, so the system can never ground an answer in documents a user is not permitted to see. Governance, audit logging, and PII redaction come built in.
A multi-model gateway routes each request to the right LLM.
Works across leading providers via Amazon Bedrock and direct APIs — Anthropic Claude, OpenAI, and others — with hybrid dense and keyword retrieval plus cross-encoder re-ranking behind the scenes.
We're onboarding a small group of design partners to test EnhanceLLM against their own knowledge sources. If you'd like to try grounded, cited answers on your organization's documents, request an invite below.
