Pipedrive's Internal AI Tools Powering 100,000+ Customers
We spoke with Agur Jõgi, Pipedrive’s CTO, about how his team is building AI into their engineering workflow.
Pipedrive is a CRM platform used by over 100,000 teams across 180 countries.
Behind the scenes, its engineering org runs more than 30 to 40 thousand customer interactions a month - and increasingly, a growing share of that ecosystem is powered by AI.
We spoke with Agur Jõgi, Pipedrive’s CTO, about how his team is building AI into their engineering workflow.
Here are the highlights from our conversation.
A completely new code review process
At Pipedrive, every pull request runs through an AI code review pipeline - three models in parallel.
“We run all our pull requests through three bots - OpenAI, Claude, and one smaller model. We constantly do champion-challenger testing.”
The results are pushed into a Slack channel called "Pipedrive Intelligence" where engineers can see how their code scored and what patterns the models spotted.
Instead of replacing human review, it’s a peer-learning feed. Engineers compare notes, learn from the AI’s comments, and discuss them in “lunch-and-learn” sessions.
It’s an evolving internal product - equal parts tooling and culture.
Juniors write faster. Seniors learn differently.
Agur saw what many teams see with generative coding tools: productivity spikes for juniors, more review pressure on seniors.
“Juniors started to write way more code. At first it slowed our seniors - they had more to review.”
But over time, seniors discovered value too. Since AI doesn’t always solve problems the way a human would, it forces veterans to rethink old habits.
“AI generates slightly different code. It makes me rethink my approach - maybe I’m stuck in my senior thinking.”
The result has been a new kind of continuous training loop, where reviewing AI-generated patterns sharpens both intuition and creativity.
Building "lazy" smart SREs
Before generative AI became mainstream, Pipedrive’s reliability engineers were already experimenting with ML to prevent incidents.
They trained models on years of Datadog logs to predict patterns that led to outages - and alert teams before they happened.
“It enabled us to start contacting customers pre-emptively, saying, ‘Hey, if you keep using the system like this, you might hit trouble.’”
Agur calls it “AI-enabled SREs” or, with a grin, lazy SREs.
“Being lazy is one of the most innovative skills a person can have. If you’re lazy and act smart, you get a bonus and still sleep through the night.”
The system now flags anomalies early enough for engineers to adjust configurations and prevent downtime - a powerful layer of resilience.
Build vs buy: learning by building
Pipedrive’s engineering excellence team builds most of its AI tools in-house. It’s slower upfront, but it deepens understanding.
“We want to understand how things work. We build it, learn, and if it doesn’t help, we throw it away.”
They’ll adopt third-party models for general-purpose tasks, but in core domains they prefer home-grown experiments. Agur frames it as a strategic trade-off between speed and literacy: the team learns faster when it builds the tools it uses.
A traffic-light system for safe experimentation
To move fast without crossing compliance lines, Pipedrive created a red-yellow-green framework that governs how teams use AI on code and data.
- Green = safe to use with public or non-sensitive data
- Yellow = consult an internal “champion” for review
- Red = requires legal or data-protection approval
“We want every team to challenge new technologies, but there must be a common rule set to avoid getting into trouble while experimenting.”
It’s a lightweight way to balance innovation with GDPR and EU AI Act requirements - giving engineers autonomy while protecting customer trust.
The long view
AI has prompted a very involved learning culture within Pipedrive's engineering teams.
When models challenge your code, flag patterns in production, or surface insights from tens of thousands of user interactions, they force teams to see their systems differently.
“It’s a deep learning curve for us - but an exciting one.”
We had such a great time jamming with Agur! You can catch our full conversation on YouTube, alongside episodes with engineering and product leaders from Intercom, Monday.com, and Vercel.