Make Yourself AI

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About Make Yourself AI

Make Yourself AI is an operations platform designed to capture the judgment, experience, and decision-making processes of a team’s best performers and translate them into software tools. It connects what experts know with the data in existing systems and transforms that knowledge into workflows, agents, and applications that teams can use. The platform starts by mapping how work is actually done, identifying what matters, what to check, and how decisions are made. It then builds these insights into tools that integrate with the systems and workflows the team already uses. The goal is to reduce the time spent on routine tasks and enable faster, more informed decisions by embedding institutional knowledge into software. Typical users include planners, operators, engineers, and finance teams who need to standardize and scale their expertise across multiple sites or teams.

Key features

  • Captures expert judgment and decision logic
  • Connects with ERP, records, and prior work data
  • Builds workflows, agents, and apps from captured knowledge
  • Supports multi-site deployment and reuse of built tools
  • Provides forward-deployed guidance and operational context
  • Measures impact against real baselines
  • Scopes integrations based on specific use cases
  • Includes a proof-of-value process with real data

Use cases

  • Automating margin analysis by combining ERP standards with operator records
  • Embedding planner constraints into ERP release commitments
  • Creating repeatable analyses for network planning based on shipment history and demand

Pros

  • Captures and formalizes expert judgment into software
  • Integrates with existing systems and workflows
  • Builds workflows, agents, and apps from real operational data
  • Reduces time spent on routine tasks by automating decision support
  • Scales institutional knowledge across teams and sites

Cons

  • No self-serve pricing; requires a discovery call to scope and price
  • Forward-deployed guidance implies on-site or close collaboration
  • Pricing and scope depend on the specific problem and systems involved

Frequently asked questions about Make Yourself AI

What does Make Yourself AI do?

Make Yourself AI is an operations platform that captures the judgment, experience, and decision-making processes of a team’s best performers and translates them into software tools such as apps, agents, and workflows. It connects institutional knowledge with existing system data to standardize and scale expertise across teams.

Who is Make Yourself AI designed for?

The platform is designed for planners, operators, engineers, and finance teams who need to standardize and scale their expertise across multiple sites or teams. It is particularly useful for those who rely on judgment and experience to make decisions in their workflows.

How does Make Yourself AI pricing work?

Pricing is scoped to the specific problem, systems, and processes involved, rather than a fixed feature grid. It starts with a conversation to identify the operational issue, followed by a discovery phase to define the scope and measures of success before a proposal is provided.

What integrations does Make Yourself AI support?

The platform integrates with existing systems and tools used by the team, connecting judgment and decision-making processes with records, data, and prior work stored in those systems. Integrations are scoped to the specific use case and operational context.

What is the typical process for getting started with Make Yourself AI?

The process begins with a discovery call to scope the problem, followed by a working proof where a focused app, agent, or workflow is built using a small data extract. The team then reviews the tool, refines it, and measures its impact before moving to production.

Can Make Yourself AI be used across multiple sites or teams?

Yes, the platform is designed to capture and reuse institutional knowledge across multiple sites or teams. Once expertise is captured in a tool at one site, subsequent sites can start with that shared context instead of rebuilding from scratch.

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