How does team-wide collaboration influence the success of AI development?

When cross-functional teams have a clear understanding of the application logic, collaboration improves, which can significantly reduce the time it takes to get products to market.

The goal for Suggestic was clear: enable their team to work together on creating prompts and establish internal benchmarks to evaluate the logic they're creating.

Now, let’s explore how Suggestic’s team used Vellum to navigate through this challenge, paving the way for a more collaborative AI development process.

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Suggestic is at the forefront of health-tech innovation, leveraging WellnessGPT, an AI-powered behavioral engagement and wellness-tech platform. With a focus on creating comprehensive health-tech experiences, Suggestic offers white-label apps and AI tools that cater to businesses of all sizes.

Founded in 2014 and currently empowering a team of 20 employees, Suggestic is committed to transforming the wellness industry through advanced technology.

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Before integrating Vellum into their workflow, Suggestic faced significant challenges with collaboration in the AI development processes. Reliance on open source frameworks and Python scripts for LLM function calls made it difficult for the product team and CEO to track development progress and understand product capabilities.

The lack of a graphical interface for product development creation further compounded these issues, limiting the involvement of non-technical team members in development and QA processes.

Upon conducting a comprehensive market survey of LLM development tools, Suggestic identified Vellum as the most complete solution to address their challenges.

We sat down with Carver Anderson, Head of Operations at Suggestic, to discuss their journey from limited engagement in the development process to a more open and collaborative environment.

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Today, Suggestic is using essentially all of Vellum's application functionality.

Their engineering and product teams are actively collaborating within various Prompt Sandboxes to iterate on their LLM use cases.

They're also using the Evaluations product to rigorously test and assess their Prompts and Workflows, ensuring they meet product and client requirements.

By using these products, Suggestic facilitated direct involvement of product managers in the development process, and significantly accelerated time to market.

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There are three major value realizations for Suggestic:

Enhanced Productivity

The adoption of Vellum exceeded Suggestic's expectations, dramatically improving productivity levels. Carver says that:

‍“We are blown away by the level of productivity we realized within days of turning on our Vellum account.”.

Improved Collaboration

Vellum enabled a clear understanding of AI application logic, ensuring alignment with functional specifications and client requirements.

“Our product team can now walk through the logic of our AI application and see how the product works as it relates to our functional specs. We know the client requirements and here we can see every branch of the logic and see how it satisfies our client's requirements.”

Empowered Product Management

With Vellum, product managers could directly tweak prompts and participate in the QA process, bridging the gap between conceptualization and implementation.

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Vellum has empowered more than 100 companies like Suggestic to make their AI development process more collaborative. By using Vellum, these companies are able to rapidly develop multi-step AI apps, assess their prompts and roll out reliable AI solutions.

If you want to utilize LLMs for your use-case, evaluate your processes, and engage your entire team in the process, we can help.

Our platform is the complete solution on the market to help with developing, evaluating and maintaining LLM-powered featured in production.

To see how our app can transform your AI development, request a demo here or contact us at support@vellum.ai.