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LitLingo Prevent
Real-time code of conduct reinforcement
Prevent damaging communications before they occur with real-time intervention that informs employees when they may be at risk of sending an out-of-policy message.
detection and prevention
Extend your existing compliance program with high-precision automation to catch problematic language before a discoverable communication is sent.
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Train in-the-moment
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Reinforce your compliance training when it matters most by notifying employees of out-of-policy content and guiding them toward more effective messaging.
Measure outcomes
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Evaluate the effectiveness of your general compliance training and in-the-moment guidance with analytics on the prevalence and types of non-compliant messages detected, prevented, and sent.
Client-side warning for in-the-moment training
LitLingo Prevent enables your organization with the ability to detect and prevent non-compliant language in real-time. When an out of policy message is detected, users receive a warning notification that contains the flagged phrase, the category the language falls under, and then guidance on what company guidelines apply to the communication. The message author then has the ability to either send anyway or go back and edit the message. The LitLingo platform then offers data into what messages are sent out of policy and can be customized to never allow out of policy language to be sent.
Server-side email warning for complete prevent coverage
For non-official messaging clients, LitLingo can identify messages that violate a language model on the server-side enabling your organization to ensure complete preventative coverage. Users who violate a policy or language rule receive a warning in the form of an email notification that gives similar guidance and options as the client-side warning modal. Easily integrate LitLingo into your existing system without having to change any other parts of your automated testing workflow or tools.
Track end-user prevent effectiveness
Identify the positive impact of training by tracking how end users change their language based on in-the-moment feedback provided by LitLingo. Use this data to understand what types of language LitLingo has prevented and how effective your training programs are.