Retrieval-Augmented Answers

Upload a set of documents and ask questions in plain language. Every answer is assembled from the passages it cites.

Status: Concept
Technology: Python Embeddings Vector database LLM API

Retrieval-Augmented Answers: Overview — Walkthrough of approved screenshots. Scroll the strip below or use the arrows.

Retrieval-Augmented Answers: Ask — Walkthrough of approved screenshots. Scroll the strip below or use the arrows.

01 The problem

General AI chat tools answer confidently from the open internet, which is unusable for questions about contracts, policies or technical manuals.

02 The approach

Documents are split, embedded and indexed. Each question retrieves the most relevant passages, and the model answers only from those passages, showing them alongside the answer.

03 Objectives

Cite a source passage on 100% of answers
Answer from documents in under 3 seconds
Say “not found” instead of guessing
Cut contract and policy research time by 60%

04 Key features

PDF, Word and web page ingestion
Passage-level citations
"Not found in sources" instead of guessing
Per-collection access control

05 Links and services

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AI Knowledge Base and Appointments Platform

A secure company knowledge base with an AI assistant, live human support and appointment booking, built for a private healthcare group and embeddable on any website with one snippet.

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