Searchdoc
Connecting Complex
Enterprise Data to AI
AI Native Search Infrastructure
Enterprise AI Search Infrastructure
Searchdoc builds AI Native Search Infrastructure that accurately understands the structure and context of complex business documents like RFPs, contracts, and technical specifications.
We handle domain-specific terminology and multiple languages, enabling AI to effectively leverage enterprise knowledge. By improving search quality, we fundamentally enhance AI service accuracy.
“Search quality determines AI service accuracy.
We solve that core problem.”
Searchdoc Team
Team Searchdoc
Search & Infrastructure Experts
Our team brings together experts from AWS, NAVER, and SAMSUNG who have designed large-scale search systems, built cloud infrastructure, and led global enterprise solutions.
Expanding AI Applications
With advancements in LLMs, AI use cases like Copilot, RAG, Agent, and Workflow are growing rapidly. Enterprises are creating more value through AI than ever before.
Limitations of RAG
The core layer for leveraging domain knowledge still relies heavily on Vector Embedding-based RAG. But with complex enterprise data, its limitations are clear.
A Search Company's Approach
We're solving this problem with the approach and expertise of a search company. Deeper search technology delivers greater business value.
Why Search?
We believe there are still many problems AI hasn't solved in each industry.
The Core Problem
The core reason AI doesn't work properly is that it cannot leverage domain information effectively. We believe search technology holds the key to solving this problem.
Real Use Cases
Building conversational search or chatbots from tens of thousands of contracts and RFP documents, automatically extracting harmful clauses and checklists, or generating new documents based on them.
Search is the Foundation
At the foundation of all this is accurate search. Search must be accurate for AI to give the right answers. We build that search.
Technology
Search is fundamentally a technology that bridges the gap between queries and documents. We must reduce the distance between a user's short question and the answer hidden within vast documents.
Document Expansion
We expand documents and extract various features. We analyze document structure, context, and domain-specific terminology to transform them into searchable formats.
Query Reformulation
We reformulate user queries. We understand intent from short questions and expand them into forms that can match documents.
Ranking Model
We calculate the distance between expanded documents and reformulated queries. We place the most relevant results at the top.
Search quality determines AI service accuracy.
Searchdoc builds that core.
An AI Native Search Infrastructure company
that finds accurate information from complex enterprise data