Validate requirements before you build
A supersonic aircraft developer builds self-serve prototype systems in Airtable to validate requirements before committing to custom software development, capturing user workflows, data structures, and business logic up front. IT and engineering teams work from a defined return-on-investment threshold that decides whether to rebuild a workflow in-house or continue running it on Airtable.
The problem
Custom software was expensive to commit to without validated requirements, and IT had long lead times to build on untested assumptions. Paper requirements did not always match how a workflow ran in practice, so building the wrong thing was a real risk.
What they built
Engineering, IT, and Program Management build self-serve prototype bases in Airtable to test workflows, data structures, and business logic before custom development. Each prototype runs long enough to show whether requirements hold up, and a return-on-investment analysis compares staying on Airtable against a custom build.
The outcome
Requirements are validated in a working prototype before any custom development commitment, lowering the risk of building something that misses how the workflow runs. IT and engineering now decide build-versus-continue against a clear return-on-investment framework rather than guesswork.
Inside the solution
Requirements Validation and Prototyping
Product & Roadmap Management
- Self-serve prototype base creation for requirement validation
- Workflow and data structure testing before custom development
- Return-on-investment analysis comparing Airtable versus custom build costs
- Costly custom software development without validated requirements
- Long lead times for IT to build internal tools
- Risk of building solutions that do not meet user needs
- Faster requirement validation before custom development
- Reduced risk of building the wrong solution
- Clear return-on-investment framework for the build decision
- Engineering
- IT
- Program Management
Requirements validation and prototyping
Stand up a self-serve prototype base for any workflow you are tempted to build custom software for, then track every requirement, data structure and acceptance criterion against what real users actually do in it. Airtable AI drafts the validation summary, the session findings digest and the return-on-investment brief, while automations open the scope of work step, flag contradicted requirements for rework and push a weekly digest of prototypes still in testing.
From assumed requirements to a validated prototype
Copy the templates
Build the self-serve prototype
Run the build-versus-continue analysis
FAQ
Frequently asked questions
The user workflow itself, the data structures it depends on, and the business logic that governs how records move through it, all tested with a working base before any custom development starts.
Once the prototype has run long enough to prove out the requirements, IT and engineering compare the ongoing cost of continuing on Airtable against the cost of a custom build to decide which path to take.
A working self-serve prototype that the intended users can interact with directly, so the requirements get tested against real use instead of a written specification.
Custom development is costly to reverse once started. Prototyping first reduces the risk of building a solution that does not match how the workflow actually needs to run.
Not for the prototyping process itself. The account is exploring AI for related program action item triage, a separate workflow that could eventually draw on prototypes validated this way.