[rohan404]

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.

[computerex]

Lots of companies don't charge for work that falls outside the estimated amount of time, you guys are far from the only ones doing that. It doesn't take AI to do that. And anyone would find your description of the methodology vague to the point of being useless.

> NLP, CNNs, and regression analysis

No one is asking you to reveal your algorithms in detail, but any information at all besides just naming 3 statistical methods would go a long way in convincing people of the validity of your assertions.

Maybe you're just using human estimators and are using NLP/CNN/regression analysis to compute their daily coffee supply.

[rohan404]

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

Apologies if it came across as vague. You're welcome to try out our pricing and timeline estimation system if you'd like to get a sense for how it works - it's all public (https://builder.engineer.ai).

That particular tool uses historical data from our user story management system and repository system to glean insights such as average amount of time taken on customizing features, complexity of features and the interactions between them, common errors, developer efficiency by feature grouping, etc. This is all then used as input data into our pricing and timeline estimation system.

Collecting this data was no small feat, we had to build a significant amount of project management and developer tooling in order to get the granularity of data required.

This is also why we're confident that we'll be able to improve our accuracy beyond 90% - as we build more projects, the data collected from that process will feed back into these models.