How do I upload PDFs/decks/spreadsheets into Structify and extract them into a structured dataset?

Quick Answer: Upload your PDFs, decks, and spreadsheets into Structify via drag-and-drop upload, cloud-storage connectors, or automated pipelines. Structify then extracts tables, text, numbers, and charts, normalizes them into a unified schema, and turns them into a structured dataset you can query in plain English and plug directly into dashboards.

Why This Matters

When critical revenue context is stuck in decks, contracts, spreadsheets, and PDFs, you’re flying blind. You can’t answer basic questions like “Which customers have renewal risk flagged in QBR slides?” or “What discount patterns show up in signed contracts?” without hours of copy-paste or bespoke scripts. Structify eliminates that manual work by treating documents as first-class data sources—so those “ugly” files become structured, queryable datasets tied back to accounts, opportunities, and campaigns.

Key Benefits:

• Turn unstructured files into usable revenue data: Extract tables, text, numbers, and charts from PDFs, decks, and spreadsheets—no manual re-keying.
• Connect document data to your existing systems: Link extracted fields to CRM accounts, opportunities, and marketing campaigns for full-funnel visibility.
• Move from one-off exports to always-fresh insights: Set up repeatable uploads and pipelines so your “document-derived” datasets stay current without babysitting.

Core Concepts & Key Points

Concept	Definition	Why it's important
Document ingestion	The process of uploading or connecting PDFs, decks, spreadsheets, and other files into Structify.	Makes “hard-to-reach” data (contracts, QBRs, CSVs, board decks) available in the same place as Salesforce, HubSpot, and ad platforms.
Extraction & structuring	Using AI to pull out tables, text, numbers, and charts and map them into clean rows and columns.	Turns messy files into datasets you can filter, join, and visualize without manual cleanup or custom scripts.
Semantic layer & entity mapping	Aligning extracted fields to shared definitions (e.g., “Account,” “ARR,” “Close Date”) across tools and documents.	Ensures consistent reporting across CRM, spreadsheets, and PDFs so dashboards don’t break every time inputs change.

How It Works (Step-by-Step)

Structify follows the same three-step flow for documents that it does for your tools: Bring in → Clean/merge/analyze → Visualize/share. Here’s what that looks like specifically for PDFs, decks, and spreadsheets.

1. Bring Your Files into Structify

   You’ve got a few ways to get documents in—no custom integration project needed.

   • Drag-and-drop upload:

     • Upload individual files or bulk-upload folders of PDFs, PPT/PPTX, Google Slides exports, XLS/XLSX, CSVs, and other supported formats.
     • Use this when you’re starting with a backlog: past QBRs, contract PDFs, pricing spreadsheets, call transcripts, board decks.

   • Connect to storage tools (recommended for ongoing workflows):

     • Connect Structify to Google Drive, OneDrive, Dropbox, or other storage systems your team already uses.
     • Configure watched folders (e.g., /Signed Contracts, /QBR Decks, /Pricing Models) so new files are automatically picked up.

   • Use existing integrations & pipelines:

     • If your files are generated from other systems (e.g., exported reports from a data warehouse, system-generated PDFs), bring them in alongside your other sources (HubSpot, Mailchimp, Snowflake, Google Analytics, etc.).
     • This keeps files and system data in one place, so you can compare “what’s in the CRM” with “what’s actually in the contract” in one query.

2. Extract and Structure the Data

   Once files land in Structify, the document engine goes to work.

   • Content detection:
     Structify identifies what’s inside each file:

     • Tables (pricing tables, utilization logs, KPI scorecards)
     • Text blocks (terms, notes, comments, narrative slides)
     • Numbers (ARR, discounts, usage counts, SLAs)
     • Charts and visual elements where relevant

   • Table & field extraction:
     For each file type:

     • Spreadsheets: Sheets and ranges are converted directly into tables. Structify reads header rows, data types, and formulas’ outputs.
     • PDFs: Semi-structured tables (e.g., line items, billing summaries) are extracted into clean rows/columns. Fields like “Customer Name,” “Total Contract Value,” “Start Date,” and “End Date” are captured as discrete fields.
     • Decks: Structify reads slide content, parses tables in slides, and normalizes them into a dataset (e.g., “QBR KPI table,” “Feature adoption table,” “Risk/Blocker notes”).

   • Normalization and cleanup:
     Structify’s AI normalizes and deduplicates values so your datasets aren’t a Frankenstein of formats:

     • Unifies date formats (e.g., 03/01/25 vs March 2025)
     • Standardizes currency and percentages
     • Cleans obvious header issues (“Acct Name,” “Account”, “Customer” → Account Name)
     • Separates multi-value fields where possible (e.g., “Product A + Product B” into two linked products)

   • Mapping to your business entities:
     To make document data usable in revenue analysis, Structify aligns it with your existing entities:

     • Match “Acme Corp” in a contract PDF with “ACME Corporation” in Salesforce
     • Link QBR slides to the right account, opportunity, or segment
     • Tie spreadsheet rows (e.g., manual win/loss logs) to CRM records

     This is where Structify’s semantic layer kicks in: definitions like “Account,” “Opportunity,” “ARR,” and “Renewal Date” are reusable, so new files map into existing models instead of creating one-off fields.

3. Turn It into a Structured Dataset You Can Actually Use

   Once extracted and mapped, your document data behaves like any other Structify dataset.

   • Explore in plain English (including in Slack):
     Ask questions like:

     • “Show me all signed contracts from Q1 with a discount > 20% and their Salesforce ARR.”
     • “Which enterprise accounts had a ‘churn risk’ note in the last QBR deck?”
     • “List customers where usage dropped 30%+ in our CSV logs but haven’t had a support ticket.”

     You can ask these directly in Structify—or without leaving Slack—then drill down with follow-ups as a conversation, not a query builder.

   • Build dashboards that don’t need manual updates:
     Use the extracted datasets to build charts and dashboards:

     • Contract value over time vs. CRM pipeline
     • Discount trends by segment from contract PDFs
     • Feature adoption trends from QBR decks and product usage spreadsheets

     As new files land in your watched folders or via connectors, Structify updates the underlying datasets and dashboards automatically. No more exporting a new CSV every week just to keep a slide updated.

   • Export and share structured datasets:

     • Export cleaned tables back to CSV, spreadsheets, or your data warehouse.
     • Share views and dashboards with leadership, RevOps, or finance so they see the live, structured view rather than static decks.

Common Mistakes to Avoid

• Treating document uploads as a one-off project:
  If you only upload a pile of legacy PDFs once, your insights go stale fast.
  How to avoid it: Set up ongoing connectors or watched folders (e.g., “Signed Contracts,” “QBR Decks”) so new documents are automatically processed and your structured datasets stay current.

• Skipping entity mapping (and getting mismatched reports):
  Uploading documents without mapping them to accounts/opportunities leaves you with isolated tables that can’t answer “why” questions.
  How to avoid it: Spend time upfront aligning extracted fields to your CRM entities and business definitions. Use Structify’s semantic layer so “Account,” “ARR,” and “Renewal Date” mean the same thing across tools and documents.

Real-World Example

A RevOps leader at a B2B SaaS company needed to know why enterprise renewals were slipping, but the real context was scattered: Salesforce held the opportunity data, while discount details lived in contract PDFs and renewal risk notes were buried in QBR decks. Historically, this meant pulling five contract PDFs, two decks, and a Salesforce export into Excel every time the CEO asked, “Which renewals are at risk and why?”

With Structify, they dropped their signed contract PDFs and QBR decks into a connected Drive folder and let the platform do the rest. Structify extracted all contract line items (including discount percentages, terms, and start/end dates) plus key QBR notes like “risk,” “blocker,” and “expansion opportunity.” It matched each record to the right Salesforce account and opportunity, then surfaced a structured dataset they could query in Slack:

• “Show all renewals in the next 90 days with > 25% discount in the contract and ‘risk’ mentioned in the last QBR.”

The result: a live dashboard of at-risk renewals combining CRM fields, contract terms, and QBR notes—with no more midnight spreadsheet rebuilds.

Pro Tip: Start with one high-impact workflow (e.g., signed contracts or QBR decks), wire a watched folder for it, and define 5–10 key fields you want pulled out (discount, term length, renewal date, risk notes). Once that dataset is solid and mapped to your CRM, extend the pattern to usage spreadsheets, customer surveys, and call transcripts.

Summary

You don’t need another fragile “export-to-Excel” ritual to answer revenue questions; you need your PDFs, decks, and spreadsheets to behave like any other data source. Structify lets you drag-and-drop or connect those files, automatically extract the tables, text, and numbers inside, map them to your existing accounts and opportunities, and turn them into structured datasets you can query in plain English and feed into dashboards. No SQL. No pivot tables. No waiting on a data engineer to parse one more PDF.

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