What document types are supported?

The platform supports PDFs, Word documents, and scanned images as part of its ingestion pipeline.

Can other systems use this document intelligence?

Yes — a RESTful API layer with role-based access control lets external systems leverage the same document chat and extraction capabilities.

Enterprise AI · Document Processing

Document Intelligence Platform

A document intelligence platform that lets users upload any document and start a conversation with it — asking questions, getting summaries, and extracting key information — with full chat history, editable agent responses, and API-first architecture for integration into existing workflows.

AIConversational AIEnterprise

End-to-end

Project details

Web + Mobile

Platforms

HIPAA / GDPR

Compliance

AWS Multi-tenant

Infra

Overview

An enterprise client needed to unlock the information trapped in their document repositories. Instead of manually reading through contracts, invoices, and reports, their teams wanted to ask questions like "What are the payment terms in this contract?" or "Summarize the key findings from this 50-page report" and get instant, accurate answers. TechSchweiz built a document intelligence platform that combines document ingestion, vector search, and conversational AI to make that possible.

Challenge

Enterprises handle thousands of documents daily — contracts, invoices, medical records, legal filings — but extracting actionable insights from them is slow and manual. Teams needed a way to upload any document and ask natural-language questions, with answers grounded in the document's actual content, not generic AI guesses.

How TechSchweiz helped

Built a full-stack document ingestion and chat platform with vector embeddings for semantic search, LLM-powered question answering grounded in the uploaded document, persistent chat history per document, editable AI responses that can be refined by users, and a robust API layer for external integrations.

What we built

Document ingestion pipeline with vector embeddings and semantic search

LLM-powered chat interface with grounded answers

Persistent chat history with editable AI responses

RESTful API layer and role-based access control

End Result

A production-ready document intelligence platform where users can upload PDFs, Word docs, and scanned images, then ask natural-language questions and get accurate, grounded answers — with chat history, editable responses, and API endpoints that let other systems leverage the same intelligence.

Project details

Industry: Enterprise AI
Services: AI · Conversational AI
Engagement: End-to-end

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Frequently Asked Questions

Answers are grounded in the uploaded document itself using vector embeddings and semantic search, so responses stay tied to the document's actual content.

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