Vellum is coming to the AI Engineering World's Fair in SF. Come visit our booth and get a live demo!
How Autobound Achieved a 20x Faster End-to-End LLM Iteration Cycle

Iterating on prompts using OpenAI's playground & Azure AI studio was challenging, until Autobound discovered Vellum.

Written by
Reviewed by:
No items found.

By leveraging the power of AI, Autobound enables sales teams to craft highly personalized, engaging emails that drive meaningful conversations and boost reply rates.

However, the journey to creating the perfect AI-generated content was not without its challenges.

Who is Autobound AI?

Autobound enables sales professionals to create hyper-personalized, relevant, and engaging outreach at scale based on millions of insights like news, social media, shared experiences, company initiatives, and more. With seamless integrations into popular sales tools like LinkedIn, Outreach, Salesloft, and Gmail, Autobound empowers sellers to boost their reply rates, build stronger relationships, and ultimately close more deals.

What brought them to Vellum?

As a company at the forefront of AI-driven sales engagement, Autobound recognized that the quality of their AI-generated content would be a key differentiator in their market.

However, they quickly realized that even state-of-the-art language models like GPT-4 struggled with the complex tasks required to generate compelling, personalized sales emails.

Autobound needed a solution that would allow them to rapidly iterate on their prompts and fine-tune their models to produce better content. Their existing workflow, which involved custom Python queries to measure latency and manage prompt versioning in Google Sheets, was a nightmare. Iterating on prompts using OpenAI’s playground and Azure AI Studio was equally challenging.

That's when Autobound discovered Vellum through a ChatGPT conversation - so meta, right?🤖

We sat down with Daniel Weiner, the founder at Autobound, to learn about their journey from a slow and complex AI development process to achieving a remarkable 20x acceleration in their LLM iteration cycle.

How does Autobound use Vellum today?

Today, Autobound leverages all of Vellum's powerful features to speed up their AI development processes.

Prompt Collaboration

But the Vellum's Prompt Sandbox has become Autobound's favorite feature. It allows them to easily test various prompts and models for their email generations.

This functionality has made prompt management a breeze, saving the team countless hours and headaches.

Improving Prompts Using Live Data

Now, it’s very easy for the Autobound team to manage their deployed prompts from Vellum Deployments.

The ability to rapidly test prompts on real-world scenarios using live data has been invaluable for Autobound. This level of testing was previously impossible with other sandbox environments, giving them a significant advantage in fine-tuning their AI content generation system.

Evaluate Prompts Across Diverse Test Cases

The team has been using Vellum Evaluations to run several experiments with their prompts, ensuring they're of high quality and have low latency. Thanks to these experiments, they could speed up and reduce latency per LLM feature. We'll cover this further in the next section.

What impact has this partnership had on Autobound?

20X Faster LLM Development

Vellum has accelerated Autobound's end-to-end LLM iteration cycle by at least 20 times, enabling them to rapidly test and refine their prompts on real-world scenarios using live data.

This level of efficiency was previously unattainable with other sandbox environments they’ve tried.

"Vellum has been a game-changer for us. The speed at which we can now iterate and improve our AI-generated content is incredible. It's allowed us to stay ahead of the curve and deliver truly personalized, engaging experiences for our customers."

Daniel Weiner, Founder @ Autobound

Reduced Latency by 5x

By iterating on their approach using Vellum, their team reduced the latency of their email generation system by an impressive 4-5x, from 30 seconds down to just 6-7 seconds per email.

This dramatic improvement was achieved by experimenting with different models, prompts, and fine-tuned models within Vellum's Prompt and Workflow platform.

As Autobound continues to push the boundaries of AI-powered sales engagement, their partnership with Vellum has become an integral part of their success story.

Want to try out Vellum?

Vellum has enabled more than 150 companies to build complex AI chatbot logic, evaluate with hundreds of test case, and deploy production-grade AI apps with confidence. If you’re looking to develop a reliable AI features, we’re here to help you.

If you want to try Vellum, book a demo here or reach out to us at support@vellum.ai if you have any questions.

We’re excited to see what you and your team builds with Vellum next!

ABOUT THE AUTHOR
Anita Kirkovska
Founding Growth Lead

An AI expert with a strong ML background, specializing in GenAI and LLM education. A former Fulbright scholar, she leads Growth and Education at Vellum, helping companies build and scale AI products. She conducts LLM evaluations and writes extensively on AI best practices, empowering business leaders to drive effective AI adoption.

ABOUT THE reviewer

No items found.
lAST UPDATED
Apr 11, 2024
Share Post
Expert verified
Related Posts
All
September 16, 2025
12 min
MCP UI & The Future of Agentic Commerce
Guides
September 16, 2025
4 min
Google's AP2: A new protocol for AI agent payments
Guides
September 15, 2025
6 min
We don’t speak JSON
LLM basics
September 12, 2025
10 min
Top 13 AI Agent Builder Platforms for Enterprises in 2025
LLM basics
September 12, 2025
8 min
Top 12 AI Workflow Platforms in 2025
Customer Stories
September 8, 2025
8
How Marveri enabled lawyers to shape AI products without blocking developers
The Best AI Tips — Direct To Your Inbox

Latest AI news, tips, and techniques

Specific tips for Your AI use cases

No spam

Oops! Something went wrong while submitting the form.

Each issue is packed with valuable resources, tools, and insights that help us stay ahead in AI development. We've discovered strategies and frameworks that boosted our efficiency by 30%, making it a must-read for anyone in the field.

Marina Trajkovska
Head of Engineering

This is just a great newsletter. The content is so helpful, even when I’m busy I read them.

Jeremy Hicks
Solutions Architect

Experiment, Evaluate, Deploy, Repeat.

AI development doesn’t end once you've defined your system. Learn how Vellum helps you manage the entire AI development lifecycle.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Email Signup
Sorts the trigger and email categories
Come to our next webinar
Description for our webinar
New CTA
Sorts the trigger and email categories

Start with some of these healthcare examples

SOAP Note Generation Agent
This agentic workflow generates a structured SOAP note from a medical transcript by extracting subjective and objective information, assessing the data, and formulating a treatment plan.
Personalized healthcare explanations of a patient-doctor match
An AI workflow that extracts PII data and match evidence then summarizes to the user why a patient was matched with a specific provider, highlighting factors like insurance, condition, and symptoms.

Start with some of these insurance examples

Insurance claims automation agent
This workflow automates the claims adjudication process in the insurance industry. It collects and analyzes claim information, assesses risks, verifies policy details, and generates a final decision along with a comprehensive audit trail.

Start with some of these agents

Personalized healthcare explanations of a patient-doctor match
An AI workflow that extracts PII data and match evidence then summarizes to the user why a patient was matched with a specific provider, highlighting factors like insurance, condition, and symptoms.
Trust Center RAG Chatbot
This AI pipeline creates a fully functional chatbot that uses a vector database. It enables you to upload internal documentation (like security policies) and instantly answer user questions grounded in those docs, complete with citations for transparency.
SOAP Note Generation Agent
This agentic workflow generates a structured SOAP note from a medical transcript by extracting subjective and objective information, assessing the data, and formulating a treatment plan.