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- เผยแพร่เมื่อ 13 ก.ย. 2023
- Here's quick demo of Nanonets AI intelligently capturing data from a legal document. If you'd like to see a complete demo of the entire workflow talk to us - bit.ly/46cPZ82
CONTEXT:
For the past 2 months now, we have been speaking with various kinds of litigation support services companies and businesses that offer messenger and attorney services across the US.
And regardless of the core services these businesses primarily focused on, they all had some common challenges:
❗ They received a considerable proportion of incoming orders via email (or fax) instead of their website or online form.
❗ Unlike orders received website forms, emailed orders had to be manually added to their legal ERP or CRM software (ServeManager, WinServe, PSToolbox etc.) to create the work orders or jobs to be carried out for their clients.
❗This manual activity is extremely time consuming and equally error-prone.
These litigation support services companies were on the lookout for tools that could automate this manual bottleneck in their otherwise smooth near-digital process.
We showed them how Nanonets could put their business processes on autopilot by improving their current workflows, handling exceptions and creating work orders (or jobs) in real time. Here's how:
1️⃣ All incoming order emails are forwarded to a Nanonets inbox.
2️⃣ Nanonets AI intelligently reads the email to pick out the relevant data.
▶ ▶ The AI can capture data from the email body, signature and attachment to pick out key information such as the law firm, secretary, type of document, due date, the required litigation support service, delivery details etc.
3️⃣ The AI then flags incorrect/inconsistent data that needs to be reviewed or writes the data on the respective legal CRM.
▶ ▶ The AI can be trained to flag exceptions - e.g. orders that are missing a delivery address or the name of the law firm.
▶ ▶ Nanonets connects to any legal CRM or ERP with an open API.
If you'd like to see a complete demo of the entire workflow talk to us - bit.ly/3t1VA2E
Which libraries were used for this extraction text ??