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

Quick Answer: You upload PDFs, decks, and spreadsheets into Structify either by direct file upload, connecting storage tools (like Google Drive), or piping them in via existing data flows—then Structify’s document processing turns them into structured tables and fields automatically. You define what you care about (e.g., pricing, terms, company names, metrics), and Structify normalizes, deduplicates, and merges that extracted data with the rest of your revenue stack so you can query it in plain English or drop it straight into dashboards.

Why This Matters

If your contracts, QBR decks, and “one-off” spreadsheets are stuck in folders, you’re missing half the story of what’s driving (or blocking) revenue. The important details—discounts, custom terms, competitor mentions, product usage tables—live in PDFs and PowerPoints, not just Salesforce fields. Structify lets you pull those documents in, extract the signal into a clean dataset, and connect it to your CRM, support tickets, and web intel so you can answer real questions like “Which discount patterns delay renewals?” without a manual data-entry sprint.

Key Benefits:

• Kill manual data entry: Replace hours of copy-paste from PDFs and decks with automated extraction into structured tables.
• Connect ‘ugly’ documents to live revenue data: Merge contract terms, QBR metrics, and spreadsheet models with CRM, marketing, and product data in one place.
• Get to analysis faster: Go from document upload to dashboards and Slack-ready answers in an hour—not weeks of wrangling.

Core Concepts & Key Points

Concept	Definition	Why it's important
Document ingestion	The process of bringing PDFs, decks (PowerPoint/Keynote), and spreadsheets (Excel/CSV) into Structify via upload or connectors.	It gets critical but messy revenue context out of folders and into a system that can actually use it.
Document extraction	Structify automatically pulling tables, text, numbers, charts, and entities out of each file.	Turns unstructured documents into structured datasets—no more retyping tables or copy-pasting from PDFs.
Normalization & merging	Using AI to standardize fields (names, formats) and match them to existing records (e.g., “Acme Corp” vs “ACME Corporation”).	You don’t end up with a second, conflicting source of truth; extracted data enriches and extends what you already have.

How It Works (Step-by-Step)

At a high level, you follow Structify’s core flow: Bring In Any Data Source → Clean, Merge, and Analyze → Visualize and Share Insights. For documents, that looks like this:

1. Ingest your PDFs/decks/spreadsheets

   Use whichever entry point matches how your team works today:

   • Direct upload:

     • Drag-and-drop PDFs, PowerPoints/Keynotes, Excel files, or CSVs into Structify.
     • Batch upload is supported, so you can drop entire folders of contracts, QBRs, or export dumps.

   • Connect storage & tools:

     • Link sources like Google Drive, SharePoint, Dropbox, or email attachments (depending on your setup).
     • Point Structify at specific folders—e.g., /Customer Contracts, /Board Decks, /CS QBRs—so new files are picked up automatically.

   • Feed exports from other systems:

     • Export data from Salesforce/HubSpot, support tools, or finance systems as spreadsheets, then upload them.
     • Use this when you’re not ready to turn on a full connector but need the data in Structify this week.

   The goal is simple: no custom integration project, no API wrestling—just get the documents in.

2. Extract structured data from the files

   Once your documents are in Structify, the platform automatically processes them:

   • For PDFs (contracts, SOWs, order forms, invoices, one-pagers):

     • Extracts tables, text, numbers, charts, and key-value pairs.
     • Identifies entities like companies, products, dates, currencies, regions, and more.
     • Pulls recurring concepts like contract value, term length, renewal date, notice period, discount, add-ons into consistent fields.

   • For decks (QBRs, board decks, sales presentations):

     • Reads slide content: headlines, bullets, callouts, embedded charts and tables.
     • Converts metrics buried in slides (e.g., “NPS: 46”, “Usage up 32% QoQ”) into structured fields.
     • Captures qualitative context (e.g., “Top churn risk reasons”, “Competitor X mentioned”) that can later be queried.

   • For spreadsheets (Excel/CSV, pricing models, exports):

     • Ingests tabular data as-is, including multiple sheets.
     • Infers column types and relationships (dates, amounts, IDs).
     • Handles messy export patterns (hidden columns, header rows, and extra summary tabs).

   Under the hood, Structify uses AI not as a buzzword but for specific jobs:

   • Normalize: Align similar concepts (e.g., “MRR”, “Monthly Recurring Revenue”, “Monthly ARR”) to a single definition.
   • Deduplicate: Merge duplicate records and match entities (“Acme Corp” vs “ACME Corporation” vs “Acme Corporation, Inc.”).
   • Merge: Connect extracted document fields to CRM records, support tickets, call logs, and web-scraped competitor data.

   End result: you get clean, queryable tables like Contracts, QBR_Metrics, Pricing_Changes, or Implementation_Risks instead of a pile of files.

3. Review, map, and use the new structured dataset

   Once Structify has extracted the data:

   • Review & refine the schema:

     • See the auto-generated fields (e.g., customer_name, contract_value, renewal_date, primary_competitor).
     • Map them into your Business Wiki / semantic layer so they align with existing definitions—e.g., “Contract Value” ties to your standard “ARR” definition instead of becoming a rogue metric.
     • Add synonyms and descriptions so future questions like “deal size” or “total contract value” resolve correctly.

   • Link to existing entities:

     • Match document-derived records to accounts, opportunities, tickets, or subscriptions already in Structify.
     • For example, link Contracts.customer_name to Accounts.name so each opportunity has its associated contract details and terms.

   • Analyze in plain English (including in Slack):

     • Ask questions like:
       • “Show me all contracts over $100k with a discount above 20% and renewal in the next 90 days.”
       • “Which QBR decks mention competitor Gong for our enterprise segment?”
       • “Which implementation plans list ‘custom integration’ as a risk factor?”
     • Get answers as interactive charts and tables, then refine with follow-up questions—a conversation, not a query builder.

   • Visualize and share:

     • Turn extracted document fields into dashboards that don’t need updating:
       • A Contract Risk dashboard combining renewal dates from PDFs + product usage from your data warehouse.
       • A Discount vs Churn view combining pricing spreadsheets + CRM win/loss data.
       • A Competitor Exposure board combining mentions in decks + competitive fields in Salesforce.
     • Share views via links, exports, or direct Slack summaries so GTM teams don’t have to hunt in folders ever again.

Common Mistakes to Avoid

• Treating document uploads as a one-time project:
  If you only upload a historical backlog and don’t set up ongoing ingestion (e.g., from a contracts folder or QBR repository), your dataset goes stale fast.
  Avoid it by: connecting the source folders or systems so new PDFs/decks/spreadsheets are automatically ingested.

• Skipping definition alignment in the semantic layer:
  Uploading documents and accepting default field names can create conflicting metrics (e.g., “Total Value” vs “ARR” vs “Contract Amount”).
  Avoid it by: mapping extracted fields to your maintained definitions in Structify’s semantic layer so every dashboard, chart, and Slack answer uses the same meaning.

Real-World Example

A mid-market SaaS company wanted to understand why large renewals were slipping even though product usage looked healthy. The clues were buried in:

• Contract PDFs with specific discount tiers and custom termination clauses.
• CS QBR decks stored in Google Drive with slides about “churn risks” and “expansion opportunities.”
• Pricing spreadsheets that tracked one-off discounts and exceptions outside of the CRM.

Historically, the RevOps team would spend days combing through these files and manually updating a spreadsheet every quarter.

With Structify, they:

1. Connected their “Contracts”, “CS QBRs”, and “Pricing Models” folders in Google Drive so all PDFs, PowerPoints, and Excel files flowed into Structify.
2. Let Structify extract tables and key fields:
   • From contracts: contract_value, discount_percent, auto_renew, termination_clause_type, renewal_date.
   • From QBR decks: churn_risk_reason, expansion_potential, escalation_status.
   • From spreadsheets: promo_discount, custom_terms_flag, implementation_cost.
3. Mapped those fields into their existing revenue ontology so “Contract Value” tied to their global “ARR” definition.
4. Asked in Slack:
   “Show me all accounts with ARR > $100k, discount > 25%, renewal in next 120 days, and QBR slides mentioning ‘pricing’ or ‘budget’ as a risk.”

Structify returned a prioritized list, plus charts showing that deeply discounted deals were 2.3x more likely to slip at renewal—context they couldn’t see in Salesforce alone. That insight drove a pricing policy change and a targeted playbook for CSMs, without adding 100+ hours of manual data entry.

Pro Tip: When you set up your first document pipeline, start with one narrow, high-impact use case (e.g., contract risk, discount governance, or competitor mentions) and define the exact fields you care about. Let Structify extract and normalize those first, validate with your GTM leaders, then expand to the rest of the document library.

Summary

You don’t need another folder of “final_final_v3.pdf” contracts that no one can analyze. With Structify, you:

• Upload PDFs, decks, and spreadsheets via drag-and-drop or connected folders/tools.
• Extract tables, text, numbers, and entities into clean, normalized datasets.
• Merge that data with CRM, support, web, and warehouse sources so you can ask real revenue questions in plain English and share the answers as live dashboards and Slack updates.

Instead of treating documents as a black box, Structify turns them into structured, governed data that feeds directly into your pipeline, forecasting, and ROI decisions.

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