Describe the work naturally
Start with outcomes, examples, SOPs, constraints and the reasons behind decisions. No nodes, schemas or implementation specs.
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 shouldn't need an engineer to translate that into software. Malleable lets them build and change their own operations in the words they already use.
For example
An operations lead writes: "Jobs more than 50 miles away need a second technician, and every new customer gets a site survey first." Malleable adds the survey and the staffing rule to the diagram, and the next job follows the new rule.
Who should build it
Your experts already know how the work should go.
Operational work depends on knowing which context matters, why an exception changes the answer, who should decide and what a good outcome looks like. That knowledge lives with the people who do the work every day.
Malleable gives them a way to turn that knowledge into a running operation they can see. They explain the process in plain English, check what the AI understood on the diagram, and refine it through feedback and real use. Nobody maintains prompts, code or connector logic.
Owned by the team
Built and changed by the team that owns the outcome.
There's no engineering backlog between the people who know the work and the operation that does it.
See what the AI understood
The diagram shows the steps, who is responsible, the decisions and the handoffs, 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.
Decide where the AI uses judgment
Experts choose which steps always run the same way, where the AI can use judgment, and which decisions stay with a person.
How it works
Set it up the way you'd brief a capable new teammate.
Malleable turns what your experts know into a dependable operation without asking them 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 diagram and correct the AI's understanding before the operation handles real work.
- 03
Launch
Give the team forms shaped to the task while Malleable handles the apps, models and handoffs behind them.
- 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 don't need to learn a generic AI tool when the operation and its forms are already shaped around the job they know.
Meter configuration at Guidewheel
Guidewheel supplied its SOP and working language. The resulting operation was shared in Slack without formal training, and the field team adopted it within two weeks.
Read the Guidewheel storyLead qualification for group orders
Sales experts define what makes a promising group-order account. Malleable applies their criteria, researches only as far as needed, and sends a short brief to Teams.
See the stepsA buyer's checklist
Ask who can own the operation after launch.
A friendly setup wizard isn't enough if every meaningful change still goes back to a technical team.
- Can your experts start from the outcome and the process documents they already have?
- Can they inspect the operation without reading prompts, code or connector settings?
- Can they change how the work runs in plain English?
- Can they decide which calls the AI makes and which stay with a person?
- Can the same team improve the operation after seeing it handle real work?
Let's pick an outcome to improve. See results in days, not quarters.
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