Artificial intelligence is becoming increasingly essential in the consumer world. While much of the conversation about AI centers on chatbots and memory-based systems, Julep AI is breaking new ground.
“Julep is an API created to build multi-step workflows,” as co-founder Ishita Jindal puts it. Julep is a platform that enables AI agents to remember past interactions and execute complex tasks. The product serves as an infrastructure layer between large language models (LLMs) and your software, providing built-in support for long-term memory and multi-step process management.
Julep’s mission is to fill the existing gaps in AI and foster collaboration between humans and AI for advanced applications.
Beyond Chatbots: Julep’s mission to push the boundaries of AI
Julep’s vision goes beyond the conventional uses of AI. The team wants to enable collaboration between humans and AI for tasks that require deep thinking and long-term involvement.
"Think about a scenario like using AI in drug discovery, to author a novel, or perform end-to-end research," says Diwank Singh Tomer, co-founder of Julep. "These are the sorts of complex and dynamic workflows that Julep wants to power."
Rather than building systems that repeat simple tasks or interact based on short-term memory, Julep is designing intelligent agents that understand context over time and across multiple steps. This creates value in enterprise settings, where these tasks are often too large or complex for traditional tooling.
Creating an innovative engineering culture
Julep’s engineering culture reflects their forward-thinking approach. They’ve embraced a mono-repository culture, open-source development, and specification-driven processes because they understand that the best way to build systems is by keeping things simple and focusing on velocity. This is why Julep believes in continuous refactoring and a pragmatic approach to problem-solving.
How Julep helps their customers
Julep is already improving industries by enabling businesses to create intelligent, AI-powered systems that solve the problems they’re facing. With the 8-factor agent methodology, Julep helps customers build integral workflows like the following.
Video Editing Workflows
Content creation companies use Julep’s AI agents to improve and streamline video editing processes. Through multi-step workflows, the platform automates tasks like video segmentation, logo overlaying, transcription, and the addition of subtitles. The platform handles these tasks as distinct steps, each managed by a dedicated prompt that defines the task and connects tools in a modular, adaptable way. Julep’s flexibility allows clients to use multiple model providers, ensuring that different AI models can be swapped in based on specific editing needs.
Personalized Content Creation
A sports magazine uses Julep to personalize content for millions of subscribers. Using AI-driven workflows, Julep collects user interaction data and continuously updates user profiles to tailor content recommendations.
This process involves workflows that span several stages, starting from data collection (using specific prompts to query interaction data), to AI-powered content selection, and ultimately delivering personalized articles or newsletters to each user.
With seamless tool integration, the platform scales as the number of users grows, ensuring that the right content reaches each user at the right time. The platform continuously updates each user's profile based on their interactions, ensuring the right content reaches them at the right time.
Zero-Knowledge Proof for Identity Verification
Julep’s AI agents are at the core of a secure identity verification process for applications that handle personally identifiable information (PII). Julep uses zero-knowledge proofs to verify user identity without exposing sensitive data.
The platform uses AI to drive workflows that securely guide users through the verification process, from uploading documents to generating cryptographic proofs, and these workflows are designed using specific prompts that ensure data privacy.
Scaling AI to manage thousands of tasks across complex workflows
However, as with any ambitious project like Julep’s, scaling AI at this level comes with its challenges.
For Julep, “at scale” means managing workflows that handle tens of thousands of tasks, running across different systems, all while ensuring that every task is retried until completion without sacrificing performance. Whether it’s automating video editing or personalizing content for millions of users, Julep needs to provide reliability even as their customers' needs grow in both size and complexity — this is no easy feat.
This is where Temporal comes in. Temporal’s expressiveness allows Julep to manage these complex workflows with fewer lines of code, making it easier to debug and maintain.
Temporal’s ability to automatically retry tasks and handle multi-step workflows ensures reliable execution of long-running tasks. By using YAML, Julep can easily customize and define workflows, whether internal or customer-facing, with workflows that can stretch to 500+ lines of code still staying well-structured and reliable.
As workflows scale to tens of thousands of tasks, Temporal’s design guarantees that tasks are retried until completion without performance loss. Additionally, Temporal’s live, interactive UI enhances observability, allowing Julep to debug and provide transparency to customers, while its deterministic workflow design and Python integration ensure system reliability and scalability.
Where Julep is headed
Julep continues to innovate. Their recent launch on Product Hunt marks an exciting milestone in their journey to redefine AI-powered workflow automation.
Their next phase of development promises to further push the boundaries of what AI can accomplish, helping businesses scale their operations and improve efficiency in ways that were once unimaginable.
To see Julep in action, head over to their GitHub repository, follow their journey on X and LinkedIn, and get involved with their open-source projects.