>Engineer.ai’s “Builder” product breaks projects into small ‘building blocks’ of re-usable features that are customized by human engineers all over the world, making the process cheaper than the average process
There doesn't appear to be AI involved. A very good business model, but no AI.
What I expected the founder to say was "we've proven people want our product, now we can scale it even further by building the ai tool we always wanted to build," but I don't see that.
This is the part that got me: "... everyone can build an idea without learning to code"
I went to my first tech conference when I was 13. One of the hot items was a tool that made programmers unnecessary. It was targeted at cheapskate businesspeople. Decades later the company is long dead. But the suckers are still out there. They think that coding is the hard part, in the same way that they think the hard part about building a house is nailing things together. But in both cases, the part you're really paying for is expertise: good development firms and good homebuilders know how to turn hazy human desires into very specific implementations, while also shaping those desires to be reasonable and achievable.
Personally, I don't believe anyone will be able to achieve full code synthesis for a very long time. I'm a firm believer that we'll always need engineers in order to code, product managers to help refine customer requirements, and so on. Having been a developer on the front lines for almost a decade, I've always believed that good software development is more akin to art than an exact science ("good code is like poetry"). That being said, I do think a lot of the stuff we end up doing as part of the SDLC is incredibly repetitive from product to product; boilerplate code, setting up basic architecture, etc.
Those are the areas that we're trying to automate as much as possible so that humans can focus only on the custom bits.
Ideas are a dime a dozen. The hard part is always the asking the right questions and collecting the right data to filter out bad ideas and refine good ideas into practical products.
You've hit it on the nail - assembling code or building a programmatically controlled ESB is not rocket science - asking the right questions, and being able to get people to spec without knowing "how to code" or "understanding" tech is much harder. This is where have spent a large portion of our time in building out the "Studio" where you can choose templates, or problem sets and then we organize "features" and "workflow" logic behind it. The entire lifecycle is designed around the idea of an assembly process rather than a consulting - so its more prescriptive (we ask a lot of questions upfront) - its important to note that you still get connected to a human product manager, and there are designers from the capacity side (we work with over 100 dev shops around the world that give us designers and developers).
Here is where we use AI/Expert/Heuristics (pls note I am not the AI expert but trying to be as transparent as possible)
- Pricing is a Supervised Learning Model.
- Custom Features are a Convolutional Neural Net + NLP.
- Resource Allocation (we tap into capacity of other dev shops) is an OR/ML combination.
- Sequencing of what to do is an ML/SL problem.
- Complexity is a Clustering Problem.
- Grading Devs is a Static Code Analysis (industry standard) + NLP Problem.
- Quality Early Warning is a Supervised Learning + Heuristics (we identify early potential problems based on a developer + feature set history analysis)
- Templates being updated based on features being added by onward customers.
---> Building Blocks
- Features are one or many building blocks
- They communicate through an ESB thats allows a smarter way of messaging between individual areas.
- The ESB allows us to "plug n play" -> today it still needs human stitching but that a scale problem we are looking to fix.
We are step 5 out of 12 steps of the way through the final vision - and the above are at varying stages of deployment (some early, some more established).
There doesn't appear to be AI involved. A very good business model, but no AI.
What I expected the founder to say was "we've proven people want our product, now we can scale it even further by building the ai tool we always wanted to build," but I don't see that.