Who needs HumanFirst

Use-cases#

HumanFirst accelerate all applications and use-cases where building accurate natural language understanding from unlabeled data, and maintaining it as project grows, is critical.

Conversational Analytics#

Annotating conversations (ASR/speech-to-text, live chat, SMS, email etc) at scale and in real-time, & providing detailed insights into the content of the conversations.

Call center triage and routing (IVRs)#

Understanding increasingly specific customer requests by discovering and training those requests from the long tail of customer conversations logs

Agent augmentation for sales and support#

Providing human agents with assistance to improve their quality, speed or onboarding time, thanks to contextual scripts, cues and overlays on top of the core communication experience

RPA (business process automation)#

Updating CRM or triggering a workflow based on user input or feedback, or providing simpler language-driven interfaces to complex internal processes

Automated sales, HR or marketing campaigns#

Personalization and automation, scaleable 1:1 campaigns

Omnichannel Messaging and AI platform features#

NLU-powered features trained by the users' themselves with their own data i.e: smart canned responses, insights and analytics

Chatbots, Virtual Assistants, Voice apps#

Increasing the coverage and accuracy of chatbots on a continuous basis with incoming data, building modularized and re-usable training datasets across verticals and domain-specific knowledge

Search, FAQs and Q&As#

Self-explanatory 🙃

Business value across the organization#

Teams

Teams of all sizes use HumanFirst to accelerate the development of their NLU-powered roadmaps

  • Identify automation opportunities and insights
  • Accelerated sales cycles
  • Faster development and time-to-market
  • Reduced development and maintenance costs
  • Increased post-deployment revenue
cross-functional
  • Product teams gain insight into customer data, validate product assumptions and gain confidence in roadmap capabilities
  • Data scientists fix labeling and classification issues, experiment and tune training data, and easily deploy data across different NLU platforms
  • Labeling teams achieve 10x labeling efficiency thanks to centralized and AI-assisted workflows
  • Engineering teams easily integrate NLU capabilities via our APIs within their products