rohan404

Out of curiosity, how did you find investors to sell to for secondaries?

Disclaimer - I'm a VP E at Engineer.ai

To clarify, while we intend to use AI to solve a variety of different problems, we're not using it for actual code synthesis (ie. building apps). Instead we are leveraging code reusability and programmatic stitching/merging for our software assembly line.

In addition to that, we are leveraging various AI/ML techniques throughout the rest of the product development lifecycle, for areas such as pricing/specing/ideation, infrastructure management/scalability, code reusability itself and matching, creator (developer/QA/design) resource matching, sequencing and dependency prioritization, and more.

Disclaimer - I'm a VP E at Engineer.ai

Apologies for that experience! We're currently working on a new iteration of that site and will be launching it very shortly. Come back soon and let us know what you think.

Disclaimer - I'm a VP E at Engineer.ai

Glad you were able to stop by at Collision! The process today is certainly not as user friendly as we'd like and can be quite time consuming. We're doing a revamp of the particular experience you saw in Toronto to streamline the process and also the ability to create clickable prototypes automatically!

Though we're still developing that tool, we intend to unveil it at WebSummit this year. Hope to see you there and get your feedback on it!

Disclaimer - I'm a VP E at Engineer.ai

All of our project timelines are generated fully automatically. Today we are hovering at around a 90% accuracy on those estimates, and are moving more and more towards solving that last 10%.

We put our money where are mouth is - for example if our system generates a spec with a timeline of 10 weeks and a price of of 10K, and we take 15 weeks, we do not charge more than 10K.

Unfortunately I can't reveal more details of how we generate those timelines automatically apart from the fact that is uses NLP, CNNs, and regression analysis as it is proprietary and core to our business.

Disclaimer - I'm a VP E at Engineer.ai

We're attempting to tackle the problem holistically. That means that we're tackling every single step of the traditional product development process. All the way from how you ideate, price, and spec, to sourcing and managing developers through to QA and infrastructure management.

For example, today, our ideation/pricing/spec tools leverage applied ML, creator management leverages facial recognition for fraud prevention, and infrastructure management uses statistical modelling.

We're trying to make code re-use a repeatable and predictable process rather than just a best practice. Today in the industry it's a purely led by developers, and very often is done solely at their discretion in a manual fashion. We're attempting to platform enforce code reuse, across autonomous distributed teams and products. Apart from just deciding what the optimal building blocks for a project are, the actual assembly or intelligent merging of these building blocks in an automated way is non trivial and mirrors modern automative assembly lines.

Disclaimer - I'm a VP E at Engineer.ai

We've been very transparent with our investors on where we are in the process of creating this platform both pre-investment and post. They actually responded to the WSJ article:

A spokeswoman for Deepcore said it has complete confidence in Mr. Duggal’s vision and team.

A spokesman for Jungle Ventures said it is a proud investor in Engineer.ai and its technology, adding that “the AI landscape is a varied spectrum.”

A Lakestar spokeswoman said it also has confidence in Engineer.ai and its team, adding that “growth in the AI space does not happen overnight.” It said Engineer.ai had been very careful in presenting its technology to Lakestar and other investors

Disclaimer - I'm a VP E at Engineer.ai

Happy to elaborate - in a nutshell what we're trying to do is automate as many parts of the traditional software development lifecycle as we can, and for whatever cannot be automated, put in place the right tooling to allow for repeatable results.

Our thesis is that most applications today have a huge amount of duplication at a code level, and process level. We're trying to use reusable building blocks (well structured libraries, templated user stories, wireframes, common errors, etc.), in order to immediately solve that duplication. That being said, we're not talking about automatic code generation, it's more about being able to assemble these reusable building blocks together at the beginning of a project so you have a better starting point. There will always be customization required for any project however, and that is a human led process.

Apart from actual development, we're also trying to automate processes around project management, infrastructure management, and QA. For example, what we've already been able to do is automatically price and create timeline estimates for a project without any human involvement, determine which creators on our network are best suited for a given project, evaluate and onboard developers on to the network, setup developer environments, and a lot more!

Disclaimer - I'm a VP E at Engineer.ai

You're more or less correct in your understanding, we're trying to get to 80%, and are certainly not there yet. The other thing to keep in mind is that there's a lot more than goes into developing software than just code.

We've already been able to automate problems such as selecting the optimal creators (developers, QAs, designers) for a given project from our capacity network, the ability to price out and estimate timelines for a project (something that takes our competitors 2-3 weeks), onboard and evaluate engineers on our platform, predict, arbitrage and scale cloud infrastructure for our client's projects, along with a bunch of other areas.

There's definitely a long way for us to go, however we have been able to show proven success on these problem areas already.

Disclaimer - I'm a VP E at Engineer.ai

AI is certainly not magic, and as an industry we're super far away from what would be considered real AI in the technical sense. That being said, AI has become a catch all term for everything as simple as linear regressions, all the way through to neural networks.

We don't claim to be able to write apps using AI, we're a platform that is trying to use AI and general automation in order to optimize the traditional SDLC. Actual code generation/synthesis is years away in my opinion and there is far more impact that can be had by going after other manual aspects of software development.