Turn documents into searchable information
PDFs. Scanned records. Meeting minutes. Reports. Policies. Contracts. Manuals. Applications. Forms. Historical archives. The information exists — finding it is the problem.
More than "chat with a PDF"
Uploading one document to an AI chatbot is easy. Building a dependable system for thousands or millions of documents is a different problem, and the difference is where most document AI projects quietly stall.
A real document intelligence workflow has a lot of steps that are not AI at all — format detection, page analysis, normalization, quality checks. AI is part of the solution. It is not the entire solution.
The pipeline is the product. The model is one stage of it.
Search the way people actually think
Traditional keyword search remains extremely useful and we do not throw it away. It is unbeatable for an exact ordinance number, a person's name, a case reference, a part number, a phrase somebody remembers verbatim.
But people do not always know the exact wording used inside a document. Semantic search lets them search by meaning instead — asking for material discussing a concept even when those particular words never appear.
A well-designed system combines both, and tells the user which one found the result.
Two pipelines, two sets of rules
The same organization can run both. What changes between them is not the technology — it is what each collection is permitted to touch.
- ingest watched share Runs on your network
- extract text native PDF layer Runs on your network
- OCR fallback image-only pages Runs on your network
- embed + index stays inside Runs on your network
- search + answer local model Runs on your network
- ingest from the CMS Runs on your network
- OCR backlog overnight batch Runs on managed
- extract metadata dates, motions, votes Runs on managed
- summarize public record, policy allows Runs on cloud
- publish index served from your site Runs on your network
- Runs on your network Runs inside your own environment.
- Runs on managed Runs on infrastructure we operate for you.
- Runs on cloud Uses a commercial cloud or AI service.
OCR for scanned documents
Many of the most valuable archives are scanned PDFs with little or no usable text layer — decades of records that are, as far as any search box is concerned, pictures.
OCR turns those image-based records into searchable content. Processing can include:
- Page OCR, including rotated and skewed scans
- Text cleanup and normalization
- Document segmentation, where one file holds many records
- Metadata identification — dates, names, reference numbers
- Search indexing and embedding generation
- Summaries and structured extraction
The original document always remains the source of truth. Extracted text is a search aid, and a good system links straight back to the page it came from.
Keep sensitive archives private
Document processing can run entirely inside your environment when that is required. For less sensitive collections it can also run on dedicated managed infrastructure, which is usually faster to stand up and cheaper for a one-time backlog.
Different document collections can follow different rules. You do not have to adopt one privacy model for everything, and the organizations that try usually end up adopting the strictest one and then not doing the project.
Common projects
- Government document archives
- Meeting records and minutes
- Policies and procedures
- Technical documentation
- Legal and administrative records
- Historical archives
- Internal knowledge bases
- Research collections
- Engineering documentation
- Large PDF repositories
You do not need an AI strategy. You need a problem worth solving.
Show us the repetitive work, the archive nobody can search, the recordings piling up, or the process that looks automatable. We will work backward from there.
The information is already in the documents
Extraction, OCR, metadata, embeddings and indexing turn an archive nobody searches into something people actually use. Tell us what is in yours.