Resolve routine support tickets and prep daily briefings
Case study | Claude Platform. Task: Case study | Claude Platform 10x growth customer growth since implementing Claude 72% reduction in time-to-qualified-lead for sales teams Lindy, an AI-powered automation platform, enables businesses to create AI agents that work alongside human teams, automating repetitive tasks so people can focus on strategic, creative work that drives real business value. Since implementing Claude Sonnet 3.5 as the default model for their agents, Lindy has achieved 10x customer growth. With Claude, Lindy AI agents: Book 40+ prospect meetings monthly while sales reps focus on high-value conversations Handle over 70% of routine support tickets, freeing teams for complex problem-solving Reduce time-to-qualified-lead by 72% for sales teams Deliver 3-5x productivity gains across customers’ go-to-market and support workflows Elevating human potential through intelligent automation Lindy set out with an ambitious vision: to build a platform where businesses could create AI agents that handle repetitive knowledge work, enabling human teams to focus on what they do best: creative problem-solving, relationship building, and strategic thinking. “The vision has always been to build an AI employee that provides both maximum capability and maximum ease of use,” explained Luiz Scheidegger, Head of Engineering at Lindy. “We wanted to give teams leverage—letting them focus on high-impact work while AI handles the repetitive tasks.” The challenge was finding an AI model that could power agents sophisticated enough to handle ambiguous tasks, make complex tool calls, and communicate naturally, all while maintaining the reliability enterprise customers demand. Claude delivers the intelligence and reliability Lindy agents need After extensive evaluation, Lindy chose Claude as their default model, and while customers can select alternative LLMs, they rarely override Claude. The decision came down to performance in real-world agent scenarios. “Claude models simply performed better in our evals and use cases,” said Scheidegger. “They were able to better navigate ambiguity in large context windows, and to make better tool calls when dealing with complex workflows such as calendar management, natural language conditions, and other critical Lindy needs.” The team found Claude particularly strong at handling deeply nested data structures requiring nuanced reasoning. Calendar handling and natural language conditions—two areas where other models struggled—became Claude’s strengths. Once customers experienced the quality of Claude-powered agents, adoption was clear. “Almost no one overrides the default LLM,” Scheidegger noted. “The vast majority of all model calls on the platform are for Claude.” AI colleagues that enhance team capabilities At Lindy, Claude powers intelligent agents that work alongside teams across three main categories: go-to-market workflows, customer support, and executive assistants. In go-to-market workflows, Lindy agents handle initial outreach and qualification, allowing sales professionals to focus on building relationships and closing deals. One SaaS startup uses a Claude-powered Lindy agent to identify leads and personalize initial outreach, booking 40+ meetings monthly. This frees their sales team to concentrate on high-value conversations and strategic account development. For customer support, Lindy agents resolve routine queries instantly, enabling human agents to tackle complex issues that require greater empathy and creative problem-solving. A bootstrapped productivity tool uses Lindy to provide 24/7 first-line support. Their AI agent resolves over 70% of routine tickets, while their support team can now focus on product feedback, customer success initiatives, and handling sensitive situations that necessitate human involvement. As executive assistants, Lindy agents handle scheduling and information synthesis, giving leaders more time for strategic decision-making. One executive founder uses Lindy to prep daily briefings and manage calendar conflicts. “It feels like I’m delegating to a real team member,” they shared. This allows them to spend more time on vision, strategy, and team development. Empowering teams to do their best work Claude has unlocked capabilities that transform how Lindy customers think about human-AI collaboration. Teams using Lindy agents see 3-5x productivity gains by eliminating tedious tasks that previously consumed valuable time.; tools: Lindy. Human role: The original post does not say how a person stayed involved.; limitations: The source is a vendor case study, which may present an optimistic view of performance. It does not specify the cost of the Lindy platform or the exact integration complexity. Human oversight mechanisms are not detailed, so organizations must define their own approval workflows for automated actions..
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Overview
Case study | Claude Platform
Workflow
Case study | Claude Platform 10x growth customer growth since implementing Claude 72% reduction in time-to-qualified-lead for sales teams Lindy, an AI-powered automation platform, enables businesses to create AI agents that work alongside human teams, automating repetitive tasks so people can focus on strategic, creative work that drives real business value. Since implementing Claude Sonnet 3.5 as the default model for their agents, Lindy has achieved 10x customer growth. With Claude, Lindy AI agents: Book 40+ prospect meetings monthly while sales reps focus on high-value conversations Handle over 70% of routine support tickets, freeing teams for complex problem-solving Reduce time-to-qualified-lead by 72% for sales teams Deliver 3-5x productivity gains across customers’ go-to-market and support workflows Elevating human potential through intelligent automation Lindy set out with an ambitious vision: to build a platform where businesses could create AI agents that handle repetitive knowledge work, enabling human teams to focus on what they do best: creative problem-solving, relationship building, and strategic thinking. “The vision has always been to build an AI employee that provides both maximum capability and maximum ease of use,” explained Luiz Scheidegger, Head of Engineering at Lindy. “We wanted to give teams leverage—letting them focus on high-impact work while AI handles the repetitive tasks.” The challenge was finding an AI model that could power agents sophisticated enough to handle ambiguous tasks, make complex tool calls, and communicate naturally, all while maintaining the reliability enterprise customers demand. Claude delivers the intelligence and reliability Lindy agents need After extensive evaluation, Lindy chose Claude as their default model, and while customers can select alternative LLMs, they rarely override Claude. The decision came down to performance in real-world agent scenarios. “Claude models simply performed better in our evals and use cases,” said Scheidegger. “They were able to better navigate ambiguity in large context windows, and to make better tool calls when dealing with complex workflows such as calendar management, natural language conditions, and other critical Lindy needs.” The team found Claude particularly strong at handling deeply nested data structures requiring nuanced reasoning. Calendar handling and natural language conditions—two areas where other models struggled—became Claude’s strengths. Once customers experienced the quality of Claude-powered agents, adoption was clear. “Almost no one overrides the default LLM,” Scheidegger noted. “The vast majority of all model calls on the platform are for Claude.” AI colleagues that enhance team capabilities At Lindy, Claude powers intelligent agents that work alongside teams across three main categories: go-to-market workflows, customer support, and executive assistants. In go-to-market workflows, Lindy agents handle initial outreach and qualification, allowing sales professionals to focus on building relationships and closing deals. One SaaS startup uses a Claude-powered Lindy agent to identify leads and personalize initial outreach, booking 40+ meetings monthly. This frees their sales team to concentrate on high-value conversations and strategic account development. For customer support, Lindy agents resolve routine queries instantly, enabling human agents to tackle complex issues that require greater empathy and creative problem-solving. A bootstrapped productivity tool uses Lindy to provide 24/7 first-line support. Their AI agent resolves over 70% of routine tickets, while their support team can now focus on product feedback, customer success initiatives, and handling sensitive situations that necessitate human involvement. As executive assistants, Lindy agents handle scheduling and information synthesis, giving leaders more time for strategic decision-making. One executive founder uses Lindy to prep daily briefings and manage calendar conflicts. “It feels like I’m delegating to a real team member,” they shared. This allows them to spend more time on vision, strategy, and team development. Empowering teams to do their best work Claude has unlocked capabilities that transform how Lindy customers think about human-AI collaboration. Teams using Lindy agents see 3-5x productivity gains by eliminating tedious tasks that previously consumed valuable time.
Reported result
The case study reports 10x customer growth for Lindy, a 72% reduction in time-to-qualified-lead for sales teams, and the resolution of over 70% of routine support tickets by AI agents. These metrics are attributed to the implementation of Claude-powered agents. No failures or negative outcomes are mentioned in the provided text.
Human role
The original post does not say how a person stayed involved.
Limitations
The source is a vendor case study, which may present an optimistic view of performance. It does not specify the cost of the Lindy platform or the exact integration complexity. Human oversight mechanisms are not detailed, so organizations must define their own approval workflows for automated actions.
Try it
I want to implement an AI agent workflow using Lindy and Claude to automate routine support tickets and lead qualification. My business context is [replaceable: e.g., SaaS startup with 500 monthly tickets]. The target users are [replaceable: e.g., support team and sales reps]. Please guide me through setting up an agent that handles [replaceable: specific task, e.g., password resets or meeting scheduling]. First, perform a dry run on a sample of [replaceable: 10 historical tickets] and show me the proposed responses without sending them. I will review the output for accuracy and tone. Once I confirm the sample results are acceptable, we will proceed to configure the live workflow. Ensure that any action involving sending emails or booking meetings requires my explicit approval before execution. If the agent encounters ambiguous requests, flag them for human review instead of guessing. Provide a checklist for measuring success, such as ticket resolution rate and time saved.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.