When we set out to create Contextual, we believed that building AI solutions should feel more like designing than coding—and that meant making it visual. That vision is now a reality in SolutionAI: a visual, collaborative environment for designing, building, and running AI-powered systems.

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While the creation of a highly visual, low-code experience for AI Solution Engineers absolutely makes their job easier, makes development faster, and makes on-time delivery a slam dunk, the additional power of our visual experience goes beyond the technology team and into how we support interactions with clients. By delivering an immensely transparent and understandable AI solution design, development, and operations environment, teams building on Contextual can easily engage clients in active discussions around what is being built, how it works, resources and models in play, and how they can manage the solution themselves moving forward.

While the power of the visual solution Contextual provides is broad, some of the top questions that get asked and easily answered or discussions that get triggered through our transparent approach include the following:

When, where, and how are you prompting AI tools in your solution?

Many ‘complete’ AI Solutions will end up calling multiple AI tools or models in various steps to achieve an outcome, a process known as AI orchestration. This AI Orchestration is core to the Contextual platform. But multiple AI steps also mean a solution has multiple areas for prompt tuning, different context windows, techniques essential in effective AI prompt engineering. and sets of data being sent to individual AI tools, and different expected payloads in response. With Contextual, AI Solution creators can easily walk their clients through the individual steps.

This image shows a complex, multi-step process of assembling and organizing data from third party systems, multiple AI model calls (to Perplexity and Claude) to further distill and summarize that data, and a series of transformations before the creation of a final ‘intelligent’ result. Despite how much power is represented in this flow, the visual nature of the experience means a client can easily track and follow each step, can drill into how data is being integrated, and can see and adjust the prompts for individual AI models. It’s all clearly labeled, easy to understand, and comfortable to follow. Further, a client could elect to update this themselves with no development expertise required.

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Where is data coming from and how is it being manipulated?

In addition to the AI calls described above, each step in gathering data from external or internal systems or sources can be identified and understood. For standard APIs, the prompt and response present as visual steps with clear conditions and query instructions or parameters. Any user can quickly click into the respective node (one of the individual boxes in the diagram above) and see exactly what it’s doing. If data changes, updating how it’s received is simple. If the solution needs more precision, queries can be quickly changed. 

What does a piece of the solution do?

Our integrated SolutionAI system helps creators of AI Solutions on Contextual achieve their goals with speed and efficiency. But in addition to helping developers create the functionality they want based on deep training on patterns, individual functional nodes and how data moves within the system, SolutionAI can also be used to easily describe and explain critical functionality in natural language that anyone can follow. Once a client understands this power they immediately feel deep control and understanding of the solution as created. There is no black box, aligning with the importance of transparency in AI systems.

The clear path from business need to working solution

The ultimate result of Contextual’s approach to low-code, visual AI solution design, development, and operations is transparency, client confidence, and trust—SolutionAI is a faster, more practical way to build AI-powered software. 

It starts with a clear business need and ends with a working solution—no data science team required.

Using natural language, partners and teams define what a solution should do. Contextual turns that intent into software: logic, data flows, AI components, and human-in-the-loop steps, ensuring critical decisions involve human oversight.

It’s not just about calling an LLM—it’s about making AI usable, repeatable, and safe to deploy in real-world environments. Consultants use SolutionAI to modernize systems, automate decisions, and deliver outcomes that matter.

Faster to build. Easier to adapt. Built for production.