Built in Paris. Trusted across Europe. Meet the minds behind Rakam.

Great systems start with great engineers.

We’re not just building AI. We’re engineering the next generation of intelligent software. Our team blends research, design, and discipline to make AI work where it matters most: in production.

Meet the Team

Precision First

Compliance Built In

Flex schedule

Impact Measured

Clarity Over Hype

Team retreat

Modern tools

Equity plan

Growth budget

Collaborative Culture

Great AI isn’t born from algorithms. It’s built by teams that understand what’s really worth automating.

“Making AI possible was just the beginning. At Rakam, our mission is to build AI that is responsible, reliable, and real.”

Jean De Bodinat

CEO & Founder

“Making AI possible was just the beginning. At Rakam, our mission is to build AI that is responsible, reliable, and real.”

Jean De Bodinat

CEO & Founder

“Making AI possible was just the beginning. At Rakam, our mission is to build AI that is responsible, reliable, and real.”

Jean De Bodinat

CEO & Founder

From Idea to impact: Context ready AI Roadmaps

Redefining possibility: AI empowers software to think, create, and evolve

Explore

Match use cases to core product challenges and market trends

Security

Efficiency

Speed

Accuracy

Status:

Updating:

Security

Efficiency

Speed

Accuracy

Status:

Updating:

Security

Efficiency

Speed

Accuracy

Status:

Updating:

Strategize

Data-driven assessment: prioritize use cases that deliver the biggest impact for your effort.

class Sampling(layers.Layer):

"""Uses (mean, log_var) to sample z, the vector encoding a digit."""

def call(self, inputs):

mean, log_var = inputs

batch = tf.shape(mean)[0]

dim = tf.shape(mean)[1]

return mean + tf.exp(0.5 * log_var) * epsilon

class Sampling(layers.Layer):

"""Uses (mean, log_var) to sample z, the vector encoding a digit."""

def call(self, inputs):

mean, log_var = inputs

batch = tf.shape(mean)[0]

dim = tf.shape(mean)[1]

return mean + tf.exp(0.5 * log_var) * epsilon

class Sampling(layers.Layer):

"""Uses (mean, log_var) to sample z, the vector encoding a digit."""

def call(self, inputs):

mean, log_var = inputs

batch = tf.shape(mean)[0]

dim = tf.shape(mean)[1]

return mean + tf.exp(0.5 * log_var) * epsilon

Execute

Transform ideas into robust, production-ready AI features.

[OUR VALUES]

How we build matters as much as what we build.

We believe great AI isn’t just about speed, but more about depth, discipline, and design. At Rakam, every decision from architecture to ethics is guided by a few simple principles that keep our work real, responsible, and resilient.