Cradl AI

@cradl download the OCR text from document doc_p7q2r and summarize the key contract terms in plain language

Downloaded OCR content from doc_p7q2r (12-page service agreement, 8,421 words). Key terms: Contract duration is 24 months starting March 1, 2024, with automatic renewal unless either party provides 90 days' notice. Monthly fee is $12,500 with annual 3% escalation. Termination for convenience requires 60 days' notice and a $25,000 early exit fee if terminated before month 18. Liability is capped at 12 months of fees paid. Dispute resolution requires binding arbitration in Delaware.

This synthesis example pairs Cradl's OCR output with the AI's reasoning to extract business insights from raw text. The Download File tool retrieves content that other Cradl tools reference by URL. Large documents may hit token limits; consider asking for specific sections rather than full summaries.

When Cradl AI beats manual invoice entry at 20+ invoices/week

A 3-person finance team at a B2B SaaS company receives 30-40 vendor invoices weekly via email and Slack. They spend 6 hours a week manually keying line items into NetSuite. Cradl AI is the right call here: set up an agent that watches a shared inbox, extracts invoice fields (vendor, date, line items, totals), and writes structured predictions the team reviews in a Switchy thread before pushing to the ERP. The OAuth2 flow means the CFO controls workspace access without sharing API keys. The threshold: if your invoices are highly standardized (same 5 vendors, identical layouts), a simpler regex scraper is cheaper. But if formats vary or you're adding new vendors monthly, Cradl's agent-based extraction adapts without re-coding. Poll Get Agent Run until status is terminal, then pull the prediction and route it to your approval workflow.

Cradl AI for high-volume NDA and MSA clause extraction

A 2-person legal ops team at a 50-employee startup reviews 15-20 inbound NDAs and MSAs each month, hunting for non-standard liability caps, auto-renewal clauses, and jurisdiction terms. They currently CMD+F through PDFs and log findings in a spreadsheet. Cradl AI works well here: upload each contract as a document, run a trained agent that extracts the 8-10 clauses you care about, then review the prediction output in Switchy alongside the original PDF link. The List Documents and List Predictions tools let you batch-check what's been processed and spot any failed runs. The trade-off: if your contract volume is under 5/month or the clauses are so bespoke that extraction confidence stays below 80 percent, manual review is faster. Above 10/month with repeating clause patterns, Cradl saves 4-6 hours weekly and catches terms human skimming misses.

When Cradl AI routes support docs faster than human sorting

A 6-person support team at a fintech handles 200 tickets daily, 40 percent of which include uploaded bank statements, tax forms, or ID scans. Agents currently open each attachment, eyeball the doc type, and tag the ticket for the right specialist queue. Cradl AI fits if you're drowning in attachment volume: configure an agent to classify doc types (W-2, 1099, bank statement, utility bill) and extract key fields (account number, date, issuer). Use Get Document to pull metadata and Download File to grab the OCR'd text, then auto-tag the ticket in your helpdesk. The 11-tool set gives you enough flexibility to handle multi-page PDFs and low-quality phone photos. The boundary: if attachments are under 30/day or your team already has a dedicated doc-processing VA, the OAuth2 setup overhead isn't worth it. Above 50/day, Cradl cuts triage time by half and routes 85 percent of tickets correctly on first pass.