Custom knowledge tools for your team
PDF OCR and document ingestion automation
Turn text PDFs and scanned pages into searchable document content with extraction diagnostics, indexing status and reprocessing workflows.
Discuss your document assistantDocument evidence
Source passages
Where the workflow breaks down
Scanned pages, poor extraction and repeated headers can undermine document search.
Agree the evidence and data boundaries
We define supported devices, languages, document access, retention and evaluation questions before implementation. Local inference and network-backed library sync are scoped separately.
What we build with you
- Combine PDF text extraction with OCR where needed.
- Track extraction quality and page-level processing failures.
- Chunk and index content while preserving page references.
How we verify the workflow
- Test representative and unsupported questions.
- Inspect evidence and source references with your reviewers.
- Verify agreed failure, access and recovery scenarios.
Start with one document collection
- 01
Define the process
Map the records, roles and exceptions in your current process.
- 02
Build the agreed scope
Implement the agreed workflow and connections with reviewable records.
- 03
Test and hand over
Run representative scenarios, record results and train the people who use it.
Connected workflows
On-device AI document assistant development
Build a native Apple document assistant with local retrieval and on-device generation, scoped to supported devices and your actual connectivity requirements.
Document library sync and identity integration
Connect an authenticated document library to a native assistant with download progress, replacement, reindexing and recoverable sync failures.
Custom AI document chatbot development
Build a PDF question-answering assistant that retrieves relevant passages, asks useful clarification questions and preserves context across follow-ups.
Before we start
Can this work without a cloud AI service?
We can scope local retrieval and on-device generation for supported hardware. Authentication, downloads and synchronization can still require connectivity. We document these boundaries and test the agreed offline scenarios.
Can every answer be trusted automatically?
Source links help users review answers; they do not guarantee correctness. We agree evaluation questions, unsupported-answer handling and human review for consequential decisions.
PDF OCR and document ingestion automation
Describe your document workflow
Tell us which documents people search, which questions they ask and where the system must run.
- Document types, languages and approximate volumes
- Example questions and access requirements
- Target devices, integrations and connectivity needs