A solo operator was firefighting volume. AI now resolves 50k monthly inquiries, freeing humans to handle complex enterprise cases.
A leading AI research lab serves a diverse user base ranging from free consumers to complex enterprise API customers.
A single-person support team struggled to manage rapidly growing ticket volumes while balancing the needs of different user segments. They required a...
“We know that an AI-native strategy will always outperform an AI-added strategy. That’s why we’ve been so excited to build Fin into our workflows and prove it can make a big impact in support operations.”
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Intercom is a SaaS company that specializes in customer communication and engagement.
Anthropic's Customer support is part of this use case:
Related implementations across industries and use cases
With response times hitting 90 minutes, staff faced burnout. AI now resolves 72% of tickets, cutting manual handling from 94% to 24%.
Support ran through one inbox as volume surged 269%. An AI agent handles the routine; the team focuses on billing, privacy, and edge cases.
AI support risked hallucinations. Now, 20 specialized models fact-check every answer against strict company policies.
With response times hitting 90 minutes, staff faced burnout. AI now resolves 72% of tickets, cutting manual handling from 94% to 24%.
Support ran through one inbox as volume surged 269%. An AI agent handles the routine; the team focuses on billing, privacy, and edge cases.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Data siloed across five products, no shared identity. A unified ID and AI trained on slang now complete cancellations inside the chat.
Agents summarized every call by hand and memorized talk scripts. Voice-to-text and real-time screen pops cut after-call work by 80%.
A solo operator was firefighting volume. AI now resolves 50k monthly inquiries, freeing humans to handle complex enterprise cases.
A leading AI research lab serves a diverse user base ranging from free consumers to complex enterprise API customers.
A single-person support team struggled to manage rapidly growing ticket volumes while balancing the needs of different user segments. They required a...
“We know that an AI-native strategy will always outperform an AI-added strategy. That’s why we’ve been so excited to build Fin into our workflows and prove it can make a big impact in support operations.”
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Intercom is a SaaS company that specializes in customer communication and engagement.
Anthropic's Customer support is part of this use case:
Related implementations across industries and use cases
With response times hitting 90 minutes, staff faced burnout. AI now resolves 72% of tickets, cutting manual handling from 94% to 24%.
Support ran through one inbox as volume surged 269%. An AI agent handles the routine; the team focuses on billing, privacy, and edge cases.
AI support risked hallucinations. Now, 20 specialized models fact-check every answer against strict company policies.
With response times hitting 90 minutes, staff faced burnout. AI now resolves 72% of tickets, cutting manual handling from 94% to 24%.
Support ran through one inbox as volume surged 269%. An AI agent handles the routine; the team focuses on billing, privacy, and edge cases.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Data siloed across five products, no shared identity. A unified ID and AI trained on slang now complete cancellations inside the chat.
Agents summarized every call by hand and memorized talk scripts. Voice-to-text and real-time screen pops cut after-call work by 80%.