AI Document Search for Companies
You have thousands of documents, but traditional search only returns filenames. Intelligent document search delivers direct answers with sources.
In a Nutshell
AI document search doesn’t just find files - it gives you direct answers with sources from your documents. It handles PDFs, scans, Office files and emails and feeds relevant content into LLM workflows (RAG). Even for non-digital or hard-to-read documents, we use state-of-the-art Multimodal AI instead of traditional OCR pipelines. This makes existing AI features across product, ops and support far more useful.
At a Glance
- Answers, not file lists: direct answers instead of just filenames
- Source-backed: every answer links back to the original document
- Scan-ready: even scanned, non-digital PDFs are processed
- LLM-ready: relevant document context is fed into AI workflows
- Scalable: built for large, mixed document collections
What We Build
We build AI-powered document search that runs in your existing stack:
- ingests and indexes PDFs, Office files, emails, scans
- handles non-digital documents with state-of-the-art AI and Multimodal AI instead of traditional OCR pipelines
- answers questions in natural language
- shows the exact source in the original document
- provides relevant results for LLM workflows (RAG)
In short: Not “find a file”, but “find and cite an answer”.
Why This Matters for Teams
- less time wasted on back-and-forth and manual research
- fewer knowledge silos trapped in people’s heads
- faster decisions across ops, product and support
- better AI answers because your documents are wired in as context
Current Use Case
WIP Use Case
View milia.ai
- Client records with hundreds to thousands of documents per mandate
- High share of scanned, non-digitised PDFs
- Mixed document types with widely varying quality
- Goal: Every AI feature in milia pulls the right document context per query and feeds it to the LLM
- Our part: Architecture + implementation for robust, scalable search
Who This Is For
- Middle management: teams spend too long hunting for information that already exists
- CTOs and engineering leads: AI search needs to be reliable, verifiable and maintainable within existing systems
- Product managers in SaaS teams: users should be able to actually leverage large document collections in chat
- AI/innovation leads: existing AI features should get much more useful with solid document context
Typical Use Cases
- Contract and policy queries with sources
- internal knowledge search across SOPs, manuals and emails
- document search as context for AI chat in products
- combination of document extraction and semantic search
Integration
We integrate into existing document stores and workflows. Typical data sources and systems: SharePoint, Google Drive, OneDrive / Microsoft 365, Notion, Teams / Slack, Linear, file servers, databases and product backends. Focus: high result quality, verifiable sources, built to scale.
Frequently Asked Questions About AI Document Search
Does it work with scans and poor document quality?
Yes. We use state-of-the-art AI for document understanding and extraction instead of traditional OCR pipelines. This makes even non-digital documents robustly searchable and citable. Depending on the data, we tune the pipeline iteratively with a focus on result quality and source accuracy. More: Multimodal AI.
What's the difference to traditional document search?
Traditional search returns files or keywords. AI document search gives you direct answers with source references in the original document, using semantic context via RAG.
Does our team need AI expertise?
No. We work with teams with and without AI expertise. Technical teams often use us to move faster from prototype to production-ready, scalable search.
Can I integrate this into my existing AI chat?
Yes. The search is integrated so that relevant document contexts are automatically retrieved per request and provided to the LLM (RAG). Typical sources include SharePoint, Google Drive or M365.
What does implementation look like?
That depends on your data sources, volume and document quality. We typically start with a clear scope and iterate from a solid pilot to a scalable solution.