Malleable compared with Parabola

Malleable vs. Parabola: visible data flows and end-to-end operations

Parabola and Malleable both let operations teams describe recurring work in plain English, inspect what the AI builds, and improve it over time. Parabola centers that system on data flowing through visible transformations. Malleable centers it on the complete operation, including judgment and handoffs.

Shared foundation

What Malleable and Parabola have in common.

Plain-English building

Both let an operations expert describe recurring work conversationally and answer follow-up questions as the system takes shape.

Visible steps

Both turn the result into an inspectable workflow instead of leaving the logic inside a chat transcript.

Messy operational data

Both can connect systems, interpret unstructured inputs, and apply repeatable logic to real business data.

Repeatable execution

Both run recurring work on demand or from a trigger and keep a history the team can inspect.

The main difference

What is the reusable system organized around?

Parabola

A Parabola flow is organized around data moving from sources through visible transformation steps to destinations. Prowork builds and documents those steps from the team's description of the data process.

Malleable

A Malleable workflow is organized around the operation's outcome, stages, rationale, constraints, judgment, and human handoffs. Data transformations are part of that broader operating model.

Side by side

What follows from that difference.

Parabola

Malleable

Operational scope

Parabola

The visible flow starts with data sources and ends at data destinations

Malleable

The visible operation spans data, judgment, systems, and people to the outcome

Definition scope

Parabola

Prowork captures how data should change, while the wider operation stays outside the flow

Malleable

Domain experts define the outcome and the full operation around the data

Situational judgment

Parabola

AI steps interpret selected data, but wider judgment needs more configured flow logic

Malleable

Bounded judgment uses the full operational context of each run

Human decisions

Parabola

The flow delivers outputs for people to review and act on elsewhere

Malleable

Handoffs keep the person's decision inside the running operation

Change scope

Parabola

Teams preview a changed data step, then assess wider operating effects separately

Malleable

Experts review each change against the complete operation

Audit context

Parabola

History explains the data flow, not every decision around it

Malleable

Rationale, runs, data changes, and human decisions remain connected