Spec-driven production-ready
code generation

Spec-driven production-ready
code generation

Spec-driven production-ready
code generation

Codeplain is an AI-powered code generation service that produces production-ready software based on requirements defined in the Plain specification language.

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Specification in Plain

intercom-client.plain

***Definitions:***

- The App is a console Python app that connects to the External App API (The App).

- The List of Users is the list of External App users.

***Non-Functional Requirements:***

- The App should be implemented in Python (The Implementation).

- The Implementation should include unit tests using Unittest framework.

- The file external_app-openapi.json describes The API.

***Test Requirements:***

- End-to-end tests of The App should be implemented in Python using Unittest framework.

***Functional Requirements:***

- The App should take credentials json as the only positional argument.

- Authenticate with The API.

- Fetch The List of Users from The API.

- Print The List of Users to the console.

Generated code

intercom-client.py

def main(args=None):

try:

logger = setup_logging()

logger.info("Application started")

args = parse_arguments(args)

credentials = load_credentials(args.credentials_file, logger)

logger.info("Credentials loaded successfully")

session = authenticate_with_intercom(credentials['access_token'], logger)

logger.info("Authentication successful")

users = fetch_intercom_users(session, logger)

print("\nIntercom Users:")

for user in users:

print(f"Name: {user.get('name', 'N/A')}, Email: {user.get('email', 'N/A')}\n")

except Exception as e:

logger.error(f"An error occurred: {e}", exc_info=True)

Explore the SaaS connector repo on GitHub

SaaS connector

Task manager

Specification in Plain

intercom-client.plain

***Definitions:***

- The App is a console Python app that connects to the External App API (The App).

- The List of Users is the list of External App users.

***Non-Functional Requirements:***

- The App should be implemented in Python (The Implementation).

- The Implementation should include unit tests using Unittest framework.

- The file external_app-openapi.json describes The API.

***Test Requirements:***

- End-to-end tests of The App should be implemented in Python using Unittest framework.

***Functional Requirements:***

- The App should take credentials json as the only positional argument.

- Authenticate with The API.

- Fetch The List of Users from The API.

- Print The List of Users to the console.

Generated code

intercom-client.py

def main(args=None):

try:

logger = setup_logging()

logger.info("Application started")

args = parse_arguments(args)

credentials = load_credentials(args.credentials_file, logger)

logger.info("Credentials loaded successfully")

session = authenticate_with_intercom(credentials['access_token'], logger)

logger.info("Authentication successful")

users = fetch_intercom_users(session, logger)

print("\nIntercom Users:")

for user in users:

print(f"Name: {user.get('name', 'N/A')}, Email: {user.get('email', 'N/A')}\n")

except Exception as e:

logger.error(f"An error occurred: {e}", exc_info=True)

Explore the SaaS connector repo on GitHub

SaaS connector

Task manager

Specification in Plain

intercom-client.plain

***Definitions:***

- The App is a console Python app that connects to the External App API (The App).

- The List of Users is the list of External App users.

***Non-Functional Requirements:***

- The App should be implemented in Python (The Implementation).

- The Implementation should include unit tests using Unittest framework.

- The file external_app-openapi.json describes The API.

***Test Requirements:***

- End-to-end tests of The App should be implemented in Python using Unittest framework.

***Functional Requirements:***

- The App should take credentials json as the only positional argument.

- Authenticate with The API.

- Fetch The List of Users from The API.

- Print The List of Users to the console.

Generated code

intercom-client.py

def main(args=None):

try:

logger = setup_logging()

logger.info("Application started")

args = parse_arguments(args)

credentials = load_credentials(args.credentials_file, logger)

logger.info("Credentials loaded successfully")

session = authenticate_with_intercom(credentials['access_token'], logger)

logger.info("Authentication successful")

users = fetch_intercom_users(session, logger)

print("\nIntercom Users:")

for user in users:

print(f"Name: {user.get('name', 'N/A')}, Email: {user.get('email', 'N/A')}\n")

except Exception as e:

logger.error(f"An error occurred: {e}", exc_info=True)

Explore the SaaS connector repo on GitHub

SaaS connector

Task manager

Write specs, not code

Write specs, not code

With Plain, your specifications become the new source code. Codeplain turns structured English into production-ready software with full test coverage—no code review required.

Automatically tested.
Maintenable by design.

Automatically tested.
Maintenable by design.

Automatically tested.
Maintenable by design.

Every Plain spec includes testing requirements. Codeplain auto-generates unit and acceptance tests, and executes them inside your infrastructure.

Collaborative AI-powered software development

Collaborative AI-powered software development

Plain specs create a shared vocabulary between engineers, leads, and product teams. It encourages modular thinking, ownership, and fast onboarding.

Specs are the new source code

Specs are the new source code

Codeplain executes directly from structured specs, with full unit and acceptance test coverage—no need to read or review generated code.

A language built for AI code generation

Plain is a structured English spec language designed to work with large language models. It abstracts complexity, making AI-assisted development reliable, testable, and maintainable.

Ready to join the future of software development?

Ready to join the future of software development?

Ready to join the future of software development?

We’re partnering with forward-thinking engineering teams who want to build faster, safer, and more predictably with AI.