Automating the research process

Creating AI automation for prospect research for B2B companies.

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Trusted by over 30+ businesses.

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"Dawid can understand exactly what you need, fill in gaps himself logically, and communicates perfectly."

Andrew Becker

CTO: The Anchor Group

"Dawid can understand exactly what you need, fill in gaps himself logically, and communicates perfectly."

Andrew Becker

CTO: The Anchor Group

"Dawid can understand exactly what you need, fill in gaps himself logically, and communicates perfectly."

Andrew Becker

CTO: The Anchor Group

"We were really impressed with his work and hope to use him in the future for AI development!"

Rahil Sachak-Patwa

CEO: Tutorchase

"We were really impressed with his work and hope to use him in the future for AI development!"

Rahil Sachak-Patwa

CEO: Tutorchase

"We were really impressed with his work and hope to use him in the future for AI development!"

Rahil Sachak-Patwa

CEO: Tutorchase

"He was reliable, communicated well, understood and executed on our goals successfully"

Alexander Ford

CEO: Measurable Genius

"He was reliable, communicated well, understood and executed on our goals successfully"

Alexander Ford

CEO: Measurable Genius

"He was reliable, communicated well, understood and executed on our goals successfully"

Alexander Ford

CEO: Measurable Genius

What I do

Custom prospect research workflow

Prospect research connected to your workflow

Discovery & Analysis

We talk about your research process. How does it currently look and how can we automate and integrate it into the tools you're using.

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

Development

I build the solution for you that uses the sources you're already using.

Security

Efficiency

Speed

Accuracy

Status:

Updating:

Security

Efficiency

Speed

Accuracy

Status:

Updating:

Security

Efficiency

Speed

Accuracy

Status:

Updating:

Integration

I integrate it with your workflow in Clay, Instantly or any other sales tool.

Who Am I

I've helped founders build their AI projects for over 2+ years now.

Contacts

Ask whatever you have in your mind

I'm here to help you ask any question you want.

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