Generative AI has opened a universe of possibilities for developers. The power to create, analyze, and automate complex tasks with a few lines of code is transformative. But as many of us have discovered, moving from a clever "hello world" in a notebook to a robust, production-grade application is a minefield of challenges. Inconsistent outputs, unpredictable schema changes, and a mountain of infrastructure complexity can turn the AI dream into a DevOps nightmare.
What if you could harness the power of generative AI without the chaos? What if you could execute complex, multi-step AI workflows as simple, version-controlled functions that return predictable, typesafe results every time?
This is the promise of Functions.do—a platform designed to make AI reliable, scalable, and ridiculously simple to implement.
Building with large language models (LLMs) often feels like building on shifting sands. The very thing that makes them powerful—their creative flexibility—is what makes them difficult to tame for business-critical applications.
Functions.do introduces a new paradigm: Functions-as-Code. It treats complex business logic—even sophisticated agentic workflows—as simple, declaratively-defined functions.
Instead of cobbling together a dozen different services, you define the outcome you want. The platform handles the rest. This approach allows you to:
You're no longer an infrastructure manager; you're a developer focused on delivering value. This is how you go from idea to a production-ready API in minutes, not weeks.
The most significant challenge in production AI is reliability. This is where the "typesafe" guarantee from Functions.do becomes a game-changer.
So, what does 'typesafe' mean here?
Typesafety ensures that the inputs you provide to a function and the outputs you receive are always correctly structured and validated.
Functions.do acts as an intelligent contract between your application and the AI. It validates the data at every step of the process.
This simple guarantee eliminates an entire class of runtime errors. Schema drift becomes a thing of the past. You can finally trust the data coming back from your AI workflows and build your application with confidence.
Let's see just how easy this is. Imagine you want to generate a "Lean Canvas" business model from a simple product idea. With Functions.do, this complex task becomes three simple steps.
import { Client } from 'functions.do';
// 1. Initialize the client with your API key
const fns = new Client({ apiKey: 'your_api_key_here' });
// 2. Define the input for a complex business function
const input = {
productName: 'Agentic Workflow Platform',
problem: ['Complex business processes are manual and error-prone.'],
customerSegments: ['Enterprise Developers', 'DevOps Teams']
};
// 3. Execute the function and get a typesafe result
async function generateBusinessModel() {
const leanCanvas = await fns.run('generate/lean-canvas', input);
// The result is a typed object. Your IDE gets autocomplete!
console.log(leanCanvas.uniqueValueProposition);
// Expected output: "The simplest way to deliver business services as software."
}
generateBusinessModel();
Notice what's missing? There's no prompt engineering, no JSON.parse() wrapped in a try...catch block, and no infrastructure setup. You simply called a function with a structured input and received a perfectly typed object in return. That's it. Your leanCanvas object has guaranteed properties like uniqueValueProposition, solution, and revenueStreams that you can access with confidence.
"But can't I just do this with AWS Lambda?"
While traditional FaaS (Functions-as-a-Service) platforms like Lambda provide the compute infrastructure, they leave the hard parts to you. Functions.do is different. It's a fully-managed, agentic framework purpose-built for executing business logic.
An "agentic" system can perform complex, multi-step tasks to achieve a goal. Functions.do orchestrates calls to various tools, data sources, and models under the hood. You ask for a business plan; it knows how to perform market research, identify competitors, and formulate a strategy, presenting it all in a structured format.
This allows you to focus on outcomes (what to do), not operations (how to do it).
The era of generative AI is here, but its potential will only be unlocked by building reliable, scalable, and maintainable applications. By abstracting away the complexity and guaranteeing typesafe results, Functions.do provides the missing piece of the puzzle.
Stop fighting with infrastructure and start building the future.
Ready to turn your complex workflows into simple, reliable APIs? Visit Functions.do to get started.