From building
internal tools
to focusing
on what matters
Companies that trust Pensero
Key outcomes at a glance
What engineering leaders achieve with Pensero
AI contribution increased
from <20% to >80%
More objective, data-driven
performance and coaching conversations
For Jordi Miró, CTO at Zynap, understanding engineering performance was never optional.
With more than two decades of experience leading engineering teams and a company operating at the intersection of cybersecurity automation and AI, decisions needed to be grounded in data. The challenge wasn’t whether to measure performance, it was how to do it without taking focus away from what actually mattered.
The challenge
Building visibility systems that don’t scale
Before adopting Pensero, Jordi already had what many engineering leaders attempt to build: a custom system pulling data from GitLab, Jira, and LLM providers to understand throughput, performance, and AI usage.
It worked, to a point but it came at a cost.
Maintaining the system required constant attention: adapting to API changes, fixing data pipelines, ensuring dashboards were accurate, and continuously evolving the infrastructure. What started as a way to gain visibility slowly became a parallel system that demanded time and resources away from the actual business.
The underlying tension was clear. Visibility into performance was critical, but building and maintaining the tooling to achieve it was not where the company should be investing its energy. Jordi didn’t need more data pipelines, he needed a way to focus on interpreting and acting on the data.
The solution
A focused product with real depth and a real partnership
Zynap evaluated several solutions in the market, including well-established providers. The decision ultimately came down to a combination of product clarity and confidence.
From the beginning, there was a strong sense of alignment. Working with a team that was close, accessible, and collaborative made a difference. Zynap wasn’t treated as just another customer, but as a partner.
On the product side, what stood out was simplicity without sacrificing depth. The integrations Zynap needed were already in place, and Pensero presented data in a way that was immediately usable. Instead of navigating complex systems, Jordi could quickly access key insights and drill down into details when needed.
This balance between usability and technical depth meant the platform could be adopted quickly, without requiring additional overhead or internal development.
The results
From maintaining systems to making decisions
With Pensero in place, the shift was immediate. Instead of spending time maintaining infrastructure, Jordi could focus on what actually matters: understanding performance and driving decisions.
Data became part of the daily workflow. Each morning starts with a review of key metrics, followed by deeper analysis where needed. These insights are then used in one-on-one conversations with team leads, grounding discussions in facts rather than opinions.
“I go every morning into the email, check the first KPIs and drill down.”
This shift has had a clear impact on how the team operates. Performance conversations are more objective, coaching is more targeted, and best practices can be identified and replicated across the organization. Rather than relying on intuition, decisions are now supported by consistent and actionable data.
Measuring AI where it actually matters
As a company deeply invested in AI, Zynap needed more than visibility into usage, the goal was to understand impact.
Before Pensero, this meant manually analyzing data from LLM providers, focusing on token consumption and fragmented signals. Now, AI adoption and its effect on output can be understood directly within the context of engineering performance.
This has made it possible to track how AI contributes to real work, identify gaps in adoption, and support teams in improving how they use these tools. The shift has been significant, with AI contribution increasing from less than 20% to more than 80%.
More importantly, this increase is not just a metric, it is tied to measurable improvements in throughput and quality, reinforcing the role of AI as a true performance lever rather than an experimental layer.
“I can see the impact that AI is having on productivity… how much it influences the code that we’re producing.”
Driving alignment between engineering and product
Another key impact has been the ability to clearly see how engineering work aligns with business priorities.
With continuous visibility into what teams are working on, misalignments can be identified early. Instead of discovering issues weeks later, Jordi and his team can now understand in real time whether effort is being directed toward the right objectives.
This has reduced friction between engineering and product teams, replacing subjective discussions with shared data. Decisions around priorities, resource allocation, and trade-offs are now based on a common understanding of reality.
The result is not just better alignment, but more productive conversations and faster course correction when needed.
“I can see the impact that AI is having on productivity… how much it influences the code that we’re producing.”
A shared understanding across the organization
One of the most important changes has been the creation of a shared layer of visibility across the organization.
Individuals can track their own progress, managers can understand team performance, and leadership has a clear view of the organization as a whole. This consistency enables better decisions at every level, from coaching and development to strategic planning.
Combined with external benchmarks, it also provides valuable context on how the organization compares to others, helping guide both ambition and expectations.
“Pensero is my compass in order to know where the team is heading to.”
