Software That Speaks English

For the last 40 years, the history of software integration has been a history of struggling with protocols.

• In the 90s, we had CORBA and SOAP. Complex, XML-heavy beasts that required massive manuals to understand.
• In the 2000s, we got REST. Simpler, but still rigid. You had to know the exact endpoint (/api/v1/users), the exact method (POST), and the exact JSON body structure. If you missed a comma, it broke.
• In the 2010s, we got GraphQL. More flexible, strongly typed, but still a language you had to learn.

In every case, if you wanted Software A to talk to Software B, you needed a human developer to read the documentation, write the glue code, handle the errors, and maintain the integration.

We are now entering a new era. The era of Natural Language Integration.

The Universal Translator

Large Language Models (LLMs) have given us a "Babel Fish" for software.

An LLM doesn't care if your API uses XML, JSON, or YAML. It doesn't care if you named your variable customer_id (snake_case) or customerId (camelCase). It doesn't care if your error messages are polite or cryptic.

It can read the documentation (or just the code signature) and figure it out.

But more importantly, it allows us—the humans—to talk to software in English.

Tools are the Vocabulary

When we build a tool on Dwizi, we are effectively teaching the AI a new word.

• When we deploy get_stock_price, we add the concept of "Market Awareness" to the AI's vocabulary.
• When we deploy send_slack_message, we add the concept of "Team Communication" to the AI's vocabulary.
• When we deploy query_postgres, we add the concept of "Organizational Memory".

We are not just building functions; we are building a language. A language where the verbs are executable code, and the nouns are live data.

The Composable Future

In this future, the nature of "Integration" changes completely.

Today, if I want to connect my Calendar to my Email, I have to wait for Google to build an integration, or I have to use a tool like Zapier and manually map fields ("Event Title maps to Email Subject").

In the Agentic future, I just give an Agent a calendar tool and an email tool. Then I tell it:

"Check my calendar for next Tuesday. Email everyone I'm meeting with and tell them I'm looking forward to it."

The Agent acts as the glue. It translates my high-level English intent into the specific low-level API calls required.

1. It calls calendar.list_events().
2. It parses the result.
3. It finds the email addresses.
4. It calls email.send() for each one.

I didn't have to write a Zapier script. I didn't have to map fields. I just spoke English.

Natural Language Programming

We are moving from "Computer Programming" (writing code that tells the computer how to do something) to "Natural Language Programming" (telling the computer what to do, and letting it figure out the how).

In this world, the most valuable asset a developer can create is not a UI, but a Tool. A clean, well-documented, deterministic capability that an Agent can pick up and use.

Your software doesn't just need a GUI (Graphical User Interface) for humans anymore. It needs an LUI (Language User Interface) for Agents.

Dwizi is the platform for building that LUI. We are the infrastructure that lets your software speak English.