A Day in the Life of the World's Most AI Forward Company
At ShippingApps, we pride ourselves at being AI forward to a degree that other companies do not. We spend an outsized amount on tokens, and have a policy that while working here you'll never be limited on spend for tools like cursor full-stop (one engineer spent $3500 on cursor in august). Right now, I'm making the claim that we are the most AI forward company in the world, and below I'm going to explain how. But first, why is this a worthy goal?
The largest expense at most tech startups is payroll, with most I'm familiar with spending around 80% on salaries + benefits. For example, an engineer costs about $75 an hour or $3,846 a week or $200,000 a year. This makes sense. They solve hard problems which have large impacts on the customer base and ultimately revenue. However, every single minute that engineer is sitting idle is costing the company $125.
Every time you can avoid that, you should. And AI is the best way to multi-thread people's time while lowering context switching costs. It's now becoming a very defined tradeoff: Spend X dollars on tokens to produce leverage on time, saving 5-10x in time-productivity-dollars. The equation looks like this:
You need to optimize this as much as possible - that's the new operating equation. And here at ShippingApps, that's literally our job. So, here's how we're doing it.
note: this applies mainly to software companies, as that's the type of company we are. Software companies are uniquely positioned to be AI-forward because so much of the ecosystem around product development for software companies is interpretable by LLMs.
Our AI-First Approach
We've implemented a comprehensive AI automation system that handles everything from error monitoring to code deployment. Here's how our workflow looks:
Sentry → Posthog → Render → Linear → Devin
When an error comes into sentry, the flow collects the relevant data from posthog and the last 100 lines of logs from render then checks Linear to see if there's an existing issue tied to this error. If not, it creates a new one and assigns it to the best dev then assigns to Devin to immediately code a fix. Cofounder also makes a judgement as to whether this is an infrastructure error (like a service being down) or something that can only be fixed by writing code. Devin then writes a PR and an engineer must merge it in the morning.
Create a flow that triggers when a new error appears in Sentry. The flow should:
- Collect the error details from Sentry
- Query Posthog for relevant user behavior data around the error
- Get the last 100 lines of logs from Render
- Check Linear to see if there's already an issue for this error
- If no existing issue, create a new Linear issue with all collected context
- Assign the issue to the most appropriate developer based on the error type
- Assign the issue to Devin for an initial code fix attempt
- Determine if this is an infrastructure error or requires code changes
This is essentially a basic AI site-reliability-engineer for simple bugs. Our infrastructure is fairly complicated, so this doesn't provide full coverage, but its pretty good for small fixes.
Linear → Github → Slack
Every workflow we build integrates seamlessly with our existing tools, creating a connected ecosystem where information flows automatically and decisions are made intelligently.
The future of software development isn't about replacing humans—it's about augmenting human capabilities with AI to achieve unprecedented productivity and innovation. At ShippingApps, we're not just building this future; we're living it every day.