The bankers boxes arrive on a Tuesday. Three of them. Sometimes five. Occasionally fifteen.
Your information request was specific: soil sampling data from 2015-2018, correspondence with the state environmental agency, any reports mentioning the northwest corner of the property. What you get is everything the opposing party’s paralegal could find that might be responsive. PDFs of faxed copies of scanned documents. Lab reports with handwritten notes in the margins. Email chains that reference other email chains. Site maps with no legend. A 1987 Phase II ESA that may or may not be relevant.
You have days to figure out what was actually sent, whether it satisfies your request, and whether the information you need is actually in there. Your associate has other cases. The paralegal is already overwhelmed. The deposition is Thursday.
The Manual Review Gauntlet
The first task is cataloging. Someone has to open every file, figure out what it is, record it in a spreadsheet. Date, document type, author, subject, page count. This isn’t reading for content yet—this is just answering “what did they send us?”
Then comes relevance assessment. Which of these documents actually relate to the contamination issue? Which sampling events matter? Which correspondence is just administrative noise? You’re looking for the documents that will tell you what happened, when it happened, who knew about it, and what they did in response.
Finally, there’s extraction. The soil data you requested is in Table 4 on page 47 of a 200-page report. The crucial email is buried in a PDF titled “Correspondence_Misc_2.” The consultant who took the samples is mentioned once, in a cover letter attached to a different report entirely. Every insight requires manual page-flipping, note-taking, and cross-referencing.
This work takes weeks. You have days.
The Real Problem: Knowledge Buried in Documents
The challenge isn’t just volume—it’s that the information you need is distributed across dozens of documents with no index, no structure, and no consistent format. The lab that performed the soil sampling in 2016 isn’t identified in the 2016 soil report—it’s mentioned in a 2018 invoice found in a different box. The consultant’s email explaining why certain wells weren’t sampled isn’t in the “Well Sampling” folder—it’s in a PDF titled “Smith_John_emails_Jan_Mar_2017.pdf” alongside 80 other messages about unrelated topics.
Environmental site work generates institutional knowledge: who did what, when, why, and with what result. That knowledge gets locked in documents. Your job is to extract it, connect it across sources, and reconstruct the site’s history before the deposition, the hearing, or the settlement conference.
Traditionally, this meant reading. A lot of reading. Highlighting. Sticky notes. Spreadsheets tracking which document said what. Timelines built manually by reading every report chronologically and pulling out events.
How Statvis Processes the Boxes
Statvis treats document processing as an infrastructure problem, not a reading problem.
When you upload the contents of those bankers boxes—scanned PDFs, original digital files, whatever format they arrived in—Statvis runs them through a structured processing pipeline. The system extracts text, identifies document types, recognizes environmental entities (sampling locations, chemicals, responsible parties, consultants, regulatory agencies), and indexes everything for retrieval.
Search works across the entire document set instantly. Not just keyword matching, but natural language queries: “When was the first time benzene was detected above the residential screening level?” or “What did the consultant recommend about the monitoring wells?” The system retrieves relevant passages from across multiple documents and shows you exactly where each piece of information came from—document name, page number, highlighted text.
Chat lets you ask follow-up questions and dig deeper without manually hunting through files. The system uses hybrid search to combine AI understanding with structured retrieval, so it finds relevant information even when it’s described in different terms across different documents.
Timeline automatically extracts events from the documents and organizes them chronologically: sampling events, consultant site visits, regulatory correspondence, report submissions. Every event includes a citation linking back to the source document and page. You can see the site’s history at a glance, then click through to verify any detail.
This isn’t about replacing professional judgment—it’s about making the information accessible so you can exercise that judgment. You still decide which documents matter, what arguments to make, which facts support your case. But you’re not spending three days just figuring out what’s in the boxes.
Built for Defensibility
The key difference between Statvis and just uploading documents to ChatGPT is the citation infrastructure. Every answer the system provides links to a specific document and page. You can click through and see the exact passage that supports the claim. If the system says the consultant visited the site on June 15, 2017, you can verify that by reading the trip report yourself.
This matters because environmental cases are built on documented evidence. You need to know not just what happened, but where that information came from, who recorded it, and when. Statvis preserves that chain of evidence. The AI assists with retrieval and summarization, but the underlying sources are always available for verification.
The institutional knowledge that was locked in those bankers boxes—scattered across reports, emails, correspondence, and lab data—becomes structured, searchable, and queryable. You can answer questions like “Show me all soil samples with PAH detections” or “Find every reference to the underground storage tank” and get results in seconds, with citations.
Days of Work, Hours of Time
The bankers boxes still arrive. The documents still need review. The information is still buried. But the extraction work that used to take weeks now happens in hours. You upload the documents, let Statvis process them, and start asking questions.
Your associate reviews the Timeline to understand the site history. The paralegal uses Chat to verify that the requested sampling data is actually present. You search for specific chemicals, date ranges, and consultant names to build your argument.
The deposition is still Thursday. But now you’re ready.
Try Statvis: Book a demo to see how document processing and Timeline work on real environmental site data.