AI case study

Efficient property communications

by
AppFolio
Context

AppFolio uses Anthropic's Claude 3 Haiku and Claude 3.5 Sonnet via Amazon Bedrock to power AI-assisted responses and task automation in their Realm-X platform. This helps property managers save time on routine communications with residents, owners, and vendors by providing AI-suggested responses and automating tasks.

Results

Achieved 3x adoption rates due to faster response times with Claude 3 Haiku; property managers saved an average of 11 hours weekly on communications; reduced message response time by an average of 26 seconds; processed over half a million AI-suggested responses.

Results not reported in the source
Published
January 14, 2025
Agent type
Employee Agents
AI provider
Anthropic
Models/tools
Not disclosed
ICE score
384
The ICE framework in this database provides a quick way to assess the feasibility and potential impact of AI use cases, with higher scores signaling more actionable opportunities.

Impact: Potential benefits to the business.

Confidence: Likelihood of achieving expected results.

Ease: Simplicity of implementation in terms of resources and time.

ICE Score: Calculated by multiplying the component scores.

Note:
Each score is AI-generated based on available data and should be viewed merely as a general guideline for deeper exploration of the use cases.
Impact
8
Confidence
8
Ease
6

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solutions powered by

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Automated customer support
Context

Tidio integrated Anthropic’s Claude model to develop their Lyro AI agent, automating customer support interactions across both live chat and email channels. They implemented a network of specialized AI agents for conversation rating, summarization, and a dynamic routing system that selects the optimal API between native Anthropic API and Google Cloud Vertex AI based on performance metrics, streamlining support workflows and enabling personalized product recommendations.

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279

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Use case
Rigid support workflows
Context

Zendesk integrated OpenAI's models to create adaptive AI service agents that autonomously manage customer conversations and execute resolution tasks. They implemented a multi-agent architecture featuring task identification, conversational RAG, procedure compilation, and procedure execution agents integrated with existing support workflows through API calls and natural language procedure definitions, while providing real-time chain-of-thought visibility. This solution transitions from traditional intent-based bots to a hybrid model of scripted and generative reasoning, streamlining customer service processes.

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2
Use case
Instant personalized support
Context

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1
Use case
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2
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Thoughts & ideas