Describe the work naturally
Start with outcomes, examples, SOPs, constraints, and the reasons behind decisions—not nodes, schemas, or implementation requirements.
AI for non-technical teams
The people who know the work should decide how AI helps.
Your operations, finance, customer, and field teams already know what good work looks like. They should not need an engineer to translate that expertise into software. Malleable lets domain experts design, run, and improve AI operations in the language of the work itself.
Expertise is the interface
Non-technical does not mean low-complexity.
The hardest part of operational work is rarely connecting two apps. It is knowing which context matters, why an exception changes the answer, who should decide, and what a good outcome looks like. Your domain experts already hold that knowledge.
Malleable gives them a visible way to turn it into a running operation. They explain the process in plain English, inspect what the AI understood, and refine it through feedback and real use—without maintaining prompts, code, or brittle connector logic.
Direct ownership
Powerful AI without a technical translation layer.
The team accountable for the outcome stays close to the design and evolution of the operation instead of handing its knowledge through a backlog.
See what the AI understood
The workflow makes stages, responsibilities, decisions, and handoffs visible so experts can spot what is missing or wrong.
Change it without a ticket
When the process changes, the people who know why can explain the update directly instead of waiting for someone else to relearn the work.
Keep control of judgment
Experts decide where AI can act, where a person must decide, and what evidence should be available in every run.
The operating model
Build AI the same way you would teach a capable new teammate.
Malleable turns domain knowledge into a dependable operation without asking experts to become software builders.
- 01
Explain
Share the outcome, the current process, examples, exceptions, and why the team handles them the way it does.
- 02
Inspect
Review the visible workflow and correct the AI's understanding before the operation carries real work.
- 03
Launch
Give the team an interface shaped to the task while Malleable coordinates the underlying models, tools, and handoffs.
- 04
Teach
Use feedback and real runs to improve the operation in the same language the team uses to improve its own process.
In practice
Adoption comes easier when the software fits the work.
Teams do not need to learn a generic AI tool when the operation and its interface are already shaped around the job they know.
Meter configuration at Guidewheel
Guidewheel supplied its SOP and working language. The resulting workflow was shared in Slack without formal training, and the field team adopted it within two weeks.
Read the Guidewheel storyRetail vendor onboarding
Retail operations experts define how vendor documents and item data should be gathered, checked, corrected, and prepared for launch.
Explore the workflowA buyer's checklist
Ask who can own the operation after launch.
A friendly setup wizard is not enough if every meaningful change still returns to a technical team.
- Can domain experts begin with the outcome and their existing process materials?
- Can they inspect the operation without reading prompts, code, or connector settings?
- Can they change how the work runs in plain English?
- Can they set clear boundaries for AI decisions and human authority?
- Can the same team improve the operation after seeing it handle real work?