At Distillery, the “Never Settle” mindset shows up in small ways every day. Teams are constantly looking for ways to improve how we work, whether that means refining a process, rethinking a workflow, or experimenting with new tools.
Recently, our Head of QA, Nicolás Silvestre, ran into a challenge many teams experience. The team had access to plenty of QA data from Jira and sprint reports, but the insights were not reaching people in a way that actually influenced decisions.
Rather than accept that gap, Nico experimented with a new way to surface the information.
When the Data Is There, but Nobody’s Looking
By Nico Silvestre, Head of QA Department
Every sprint, the defects keep coming. As QA Lead, those aren’t just numbers to me. They tell a story. They show where we’re strong, where we’re fragile, and exactly where we need to grow.
For a long time, I tried to surface this data through:
Detailed emails (Archived)
Complex spreadsheets (Unopened)
Data walk-throughs (Eyes glazed over)
The information was there, but it wasn’t landing. I realized the problem wasn’t the data. It was the delivery.
Introducing: Sherlock
I decided to stop accepting that important information could stay invisible simply because the presentation was boring. I used AI to bridge the gap between technical Jira logs and actionable insights.
The goal is simple: Detect. Diagnose. Improve.
What Sherlock Tracks
Root Cause Analysis
Identifies the weakest link in our process so we can address it directly.
Resolution Rate
Shows whether we are delivering on the commitments we make to customers.
Trend Mapping
Gives the team real-time visibility into whether we are improving sprint after sprint.
Someone asked me, “Nico, did you really need to build a custom tool for this?”
Maybe not, if I had been comfortable with the status quo. But the standard reporting wasn’t landing, and I wasn’t willing to let important insights stay invisible.
I also genuinely enjoyed the process of building it. The ideas were flowing, and with the help of AI I could turn them into something real immediately.
Sherlock took just a few days to build and cost almost nothing. What mattered most was the impact. It changed the conversation around our data.
Testing is not just about catching bugs. It creates a feedback loop that helps the entire team stay aligned on the reality of the product. The same information that helps fix problems today can guide preventative improvements tomorrow.
There is a bigger lesson here too. AI is making it much easier to close the gap between recognizing a problem and building a solution. Teams no longer need to wait for a perfect third-party tool to move things forward. Sometimes the best answer is to build something small and useful yourself.
Why This Matters
Stories like this are a good example of how many teams are starting to use AI. It is not always about large platforms or long implementation cycles. Sometimes the biggest improvements come from small tools built by engineers who understand the problem deeply.
At Distillery, our teams regularly experiment with ways to make development, QA, and data workflows more effective. That mindset helps our teams move faster and helps our clients solve problems in practical ways.
If your team is exploring ways to improve product quality, engineering workflows, or how data is used across the organization, we are always happy to talk. Contact us to learn more about how Distillery supports engineering teams.