Corporate Tools LLC Innovation & Technology Culture

What’s one key communication habit you’ve developed and encouraged among your team? 

One key communication habit I’ve developed is practicing radical candidness. I had my leadership team and future leaders all read Radical Candor by Kim Scott, which emphasizes being candid with your employees and teammates while caring personally about each other. It’s about giving honest feedback that’s direct yet empathetic. This is something I think all leaders and humans in general struggle with, so it’s so useful to integrate these lessons into our daily lives.

For example, during alignments or one-on-ones, we provide straightforward feedback without sugarcoating it while ensuring it’s delivered kindly. If someone’s code quality needs improvement, we address it directly and offer support to help them improve it. 

Why is radical candidness an important habit to cultivate, and what effect has it had on your team culture?

I think this habit is crucial because it promotes transparency and trust, both of which we’re lacking in the world right now. Being candid yet understanding makes feedback honest and constructive while removing fear of criticism. This puts the focus on growth instead. 

The impact on our team culture has been significant. It has created an environment in which our team members feel safe to speak up, knowing their input will be valued. This new openness has led to better collaboration and mutual respect.
 

What advice do you have for other engineering managers who are looking to create healthy communication habits among their teams?

My advice for other engineering managers ties back to embracing radical candor. If you encourage your team to be open and honest while also being kind, you can create an incredibly healthy communication culture at your company.

Describe a project you’re especially eager to tackle in the new year.

I’m really excited to tackle a major scalability challenge with our mail processing system. We’ve got an automated pipeline that works great for processing millions of mail items for companies across the United States, but we’re hitting some serious bottlenecks, as we are receiving more and more mail every year. The big project this year is rebuilding core parts of the system to handle 10 times the volume: We’re talking about scaling up to process tens of millions of documents efficiently while maintaining the same level of accuracy in routing mail to our customers’ virtual mailboxes. 

What technologies and/or practices is your team leveraging to tackle this project?

We’re overhauling the architecture with a focus on horizontal scaling, using Kafka to handle the massive message throughput, Kubernetes for better orchestration and resource management, and Ruby for our application logic. The key is optimizing our LLM integration for high-throughput document processing while maintaining accuracy. We’re also implementing better caching strategies and potentially moving some of the heavier AI processing to more efficient patterns that can handle the increased load without breaking the bank on compute costs.

How does this project tie into larger company goals?

By solving these scalability issues, we can improve the reliability and speed of this service for our existing customers who are already pushing our current limits. It’s basically removing the technical ceiling on our business growth for this product, ensuring we can handle whatever volume comes our way without degrading service quality.

What’s your rule for releasing fast — and what KPI proves it works?

We don’t make engineers wait around to ship good code. When something is done, it should be releasable. The rule is pretty simple: Add the right integration-level test coverage, quality assurance test when the change warrants it and don’t turn deployment into a ceremony. Some teams ship right away. Some batch the week’s work and deploy together. Both are fine. The KPI is boring, which is the goal — we ship often and we almost never have to roll back.

Which standard or metric defines “quality” in your stack?

Quality is when engineers aren’t scared to ship. We use the normal stuff, GitLab pipelines, integration tests, production errors and QA when needed — but rollback rate is the one I like because it’s hard to fake. If teams are shipping fast and we’re not rolling things back, that tells me the system is doing its job and the code quality is high.

Name one recent AI/automation shipped and its impact on the team or business.

We built our own AI chat system in-house, running an open-source model on our own GPUs in our own data center. The important part is that it doesn’t just answer questions. It can actually do work through hundreds of MCP tools. Customers can buy a domain, set up hosting, fill out documents and move through real workflows inside chat. That took a team of engineers about four months and it turned AI from a fun side feature into something that can actually move the business and automate work for our customers.