Every pipeline we build is bespoke: shaped around your sources, your schema, and your rules. Underneath it is a standard foundation we bring to every engagement, so you get custom work without a custom science project. It runs on infrastructure you control, and you can see where the data goes.
Infrastructure you control
Gather public and authorized web sources reliably, with AI handling the messy parts.
Turn PDFs, scans, Office files, and email into clean, searchable text with lineage.
Pull the fields that matter into tables you can query, review, and audit.
Chat and agents that cite your documents instead of guessing.
Most AI projects stall at layer one or two. That's the part we like.
You get a pipeline that fits how your organization actually works, built on parts we've proven before. That makes it faster to stand up, easier to audit, and easier for your team to maintain.
Websites change layouts, hide data behind scripts, and paginate in creative ways. Brittle scrapers break quietly and leave gaps nobody notices until a report is wrong. We build collection systems that use browsers the way a person does, then use language models to read what came back: finding the table, classifying the page, and flagging when something looks off.
Edgett has built browser-based scraping platforms in Docker, a custom proxy layer for collection at scale, and serverless collectors in Node, mostly to automate document collection that teams used to do by hand.
Market-access and pricing teams, compliance monitoring, research groups, and anyone whose analysts still copy and paste from websites.
We build for public and authorized sources and design with each site's terms, rate limits, and your legal team's guidance in mind.
The hard part of AI over documents isn't the model. It's the scanned fax from 2009, the 400-page filing, the spreadsheet saved as a PDF, and the email with six attachments. We build ingestion pipelines that handle the mess: OCR where it's needed, layout-aware parsing where it matters, page classification, deduplication, and metadata that tracks every chunk back to its source page.
Edgett has built OCR integration and custom page classification for personal-injury claim review, an Elasticsearch-based document management system, and automatic PDF highlighting that sped up review of medical and legal records.
Legal and claims teams, health-system operations, finance and audit, and any organization with a document backlog nobody can search.
Search finds the document. Extraction turns it into data you can use. We design extraction pipelines that combine document AI, language models, and plain rules to pull specific fields into a schema you define, then route anything uncertain to a person before it reaches a report.
For our customer Ebullience, Palm Hill built a platform that extracts executive compensation from SEC filings using retrieval and multi-step extraction. Customer work: Ebullience
Edgett also led a contract validation product that checked agreements between drug manufacturers and health insurers.
Finance and investor research, contract and compliance review, healthcare market access, and claims processing.
A chat window on top of a general model will answer confidently whether or not it knows. Retrieval-augmented generation (RAG) fixes that by searching your own documents first and answering from what it finds, with citations. We build the full stack: the vector store, the retrieval logic, the chat or agent interface, and the guardrails, running on infrastructure you choose.
NoPilot: Early access · WolfPackAI: Alpha · pilots available
Edgett created Blazor Chat, an open-source interface for local language models with retrieval-augmented generation, and has run open-weight models since early 2023.
Law firms that want answers from matter files, health systems with policy and procedure libraries, banks with internal knowledge bases, and operations teams tired of searching six systems.
If the answer can't point to a page, it shouldn't be in the answer.
| Layer | Default | Options |
|---|---|---|
| Collection | Runs on your servers or private cloud | Managed cloud workers if you prefer |
| OCR and parsing | Open-source, on your infrastructure | Azure Document Intelligence or similar cloud services, when you approve it |
| Embeddings | Local embedding models | Cloud embedding APIs in your own cloud account |
| Vector store | Your database, your servers | Private cloud |
| Language model | Open-weight models on your hardware | Closed models through your own cloud account |
We'll tell you plainly which step, if any, sends data outside your walls, and we can design it so none do.
Discovery, then architecture, then deploy, then optimize. We start with a small, real document set and one question the business actually needs answered. We tailor the pipeline to it on our standard foundation, and we measure accuracy before we scale.
The parts that touch your data are: sources, schema, rules, permissions, and outputs. The foundation underneath is standard on every build, including containerized deployment, lineage, logging, human review, and testing. That's why it's faster to stand up and easier to maintain than a from-scratch project.
RAG searches your documents at the moment a question is asked and answers from them, so new documents count right away and answers can cite sources. Fine-tuning changes the model itself, which is slower to update and harder to audit. Most business document work starts with RAG.
Yes. Collection, OCR, embeddings, the vector store, and open-weight language models can all run on-prem or in your private cloud. Where a cloud service would help, we'll show you the tradeoff and you decide.
We work with scans, forms, and tables every day. Handwriting quality varies, so we test on your real samples and route uncertain extractions to a person.
Every value links to its source passage, gets a confidence score, and goes through validation rules. Anything uncertain or high-stakes goes to human review before it's used.
It depends on the source, its terms, and what you collect. We build for public and authorized sources and work within your legal team's guidance.
Usually the one closest to what you already run. Elasticsearch and PostgreSQL both handle vector search well, and we only add a dedicated vector database when scale calls for it.
