Female Founder Turns a Million-Dollar Data Entry Problem Into a Simple AI Solution

Summary

Lead With AI – Episode Summary
Female Founder Turns a Million-Dollar Data Entry Problem Into a Simple AI Solution
Host: Dr. Tamara Nall
Guest: Nermeen Cosmi, Founder & Project Manager at Datrix
Release Date: January 6, 2026

Episode Overview
In this episode, Dr. Tamara Nall sits down with Nermeen Cosmi, the founder of Datrix, an AI startup revolutionizing the way businesses handle data entry and analysis. The discussion centers on how Datrix automates and simplifies the once-tedious world of manual data entry—bringing clarity, reliability, and a fun, user-friendly experience to the process. Nermeen shares her founder journey, gives a technical peek under the hood, discusses practical user stories, and highlights the importance of ethical data handling.

Key Discussion Points & Insights

Nermeen’s Background and the Birth of Datrix

Nermeen started her tech career as an AI developer before moving into project management and client-facing roles.
The idea for Datrix emerged during a collaboration with a major textile client, where manual data wrangling slowed down critical automation projects.
“A huge chunk of our time was going after, you know, fetching data and asking them for data, having clean data...I was like, why don't we use a data pipeline or something that can automate that AI data entry process for us?” (Nermeen, 03:22)
Not finding an adequate tool on the market, Nermeen and friends built an MVP during a hackathon, which laid the foundation for Datrix.

What is Datrix and How Does It Work? [04:34–07:28]

Datrix is a web-based SaaS platform for automating data entry.
- Integrates (currently) with Gmail and Airtable: users connect Datrix to their Gmail, from which the AI agent extracts data from POs, receipts, PDFs, and organizes it within the connected database.
- Chatbot interface for uploading and parsing other document types (PDFs, CSVs, Word docs).
- “The agent would go ahead, study the [document], and it would then go to your database and pick out the schema...based on that schema, it goes ahead and matches the information and then inputs the data for you.” (Nermeen, 05:51)
- Data analysis is presented in a fun, sticky-note-style dashboard, supporting visualizations like pie and bar charts for on-the-fly insights.
- “We wanted it to be fun...Girls usually have [blackboards with] sticky notes...So for analysis Dashboard, what we did was we designed the UI to be like sticky note information.” (Nermeen, 06:37)

Target Users and Early Adoption [07:28–10:02]

Datrix targets small and medium retail businesses managing regular purchase and sales transactions, with a roadmap to serve larger enterprises.
Early testers included a construction chemicals business (Nermeen’s father’s company) and a local bakery, both of whom provided enthusiastic feedback and feature requests.
“We had like over 50 people sign up for a wish list, so that was really exciting.” (Nermeen, 09:45)

Tackling Technical Challenges & Finding Magic Moments [11:21–12:51]

The hardest technical hurdle was integrating with Gmail securely and reliably.
Nermeen’s “goosebumps moment” came when the email integration worked and Datrix could process dummy purchase orders accurately—a simple idea with massive economic impact.
“My wow moment was how simple this idea is...when I finally coded the script for the email integration...that’s when I realized it can be huge.” (Nermeen, 11:24)
A client reportedly spent “literally millions per annum on data entry and data automation.” (Nermeen, 12:19)
Driving motivation: making people’s work lives simpler, akin to the everyday revolution of inventing chairs.

Ethical Guardrails and Data Security [12:51–14:48]

Datrix is intentional about not using reinforcement learning to avoid data from one client being used to train a broader model or LLM.
Data is processed purely for extraction and input—no ongoing learning or retention across accounts.
The team is proactively studying compliance standards and international data security certifications.
“We want to make sure not to use any of the reinforcement learning models.” (Nermeen, 13:51)

How Datrix “Thinks”: No Model Training or Data Retention [14:11–14:48]

Datrix ingests a database’s schema (“column name”) and uses vector-based matching to pull corresponding fields from documents—pure matching, not ongoing model training.
“It does not train. It does not need to train because its job is to pretty much like find...the column name…it would go to your P.O. and would search for something similar...” (Nermeen, 14:14)

Notable Quotes & Memorable Moments

“In every company, there's a hidden monster. It's called paperwork.” (Dr. Tamara Nall, 00:00)
“My wow moment was how simple this idea is and why did it not exist?...when I finally...coded the script for the email integration...that's when I realized it can be huge.” (Nermeen, 11:22)
“I want to make life simpler generally. Do you know what I mean? Like how chair was invented...I want to have that simplicity that kind of like helps people.” (Nermeen, 12:39)
“We want to make sure not to use any of the reinforcement learning models.” (Nermeen, 13:51)

Key Timestamps

02:33 – Nermeen’s background and the problem that inspired Datrix
04:34 – How Datrix works (technical breakdown and platform design)
07:28 – Target customers and how small businesses use Datrix
08:46 – Early user testing stories and initial feedback
11:21 – Technical hurdles, “goosebumps” moments, and validation
12:51 – Addressing data ethics and compliance
14:11 – Technical details about data processing and privacy

Summary & Takeaway
This episode spotlights Datrix—an intuitive, no-nonsense AI tool designed to free business owners and teams from the mundane, error-prone misery of manual data entry. Nermeen’s personal journey and direct, approachable explanations make the potential and practicality of Datrix clear, demystifying the process for non-technical listeners. The conversation also addresses crucial questions of privacy, ethics, and the incremental, user-driven path to innovation—offering inspiration for founders and business leaders exploring AI’s real-world impact.

Summary

Key Discussion Points & Insights

Nermeen’s Background and the Birth of Datrix

Nermeen started her tech career as an AI developer before moving into project management and client-facing roles.
The idea for Datrix emerged during a collaboration with a major textile client, where manual data wrangling slowed down critical automation projects.
“A huge chunk of our time was going after, you know, fetching data and asking them for data, having clean data...I was like, why don't we use a data pipeline or something that can automate that AI data entry process for us?” (Nermeen, 03:22)
Not finding an adequate tool on the market, Nermeen and friends built an MVP during a hackathon, which laid the foundation for Datrix.

What is Datrix and How Does It Work? [04:34–07:28]

Datrix is a web-based SaaS platform for automating data entry.
- Integrates (currently) with Gmail and Airtable: users connect Datrix to their Gmail, from which the AI agent extracts data from POs, receipts, PDFs, and organizes it within the connected database.
- Chatbot interface for uploading and parsing other document types (PDFs, CSVs, Word docs).
- “The agent would go ahead, study the [document], and it would then go to your database and pick out the schema...based on that schema, it goes ahead and matches the information and then inputs the data for you.” (Nermeen, 05:51)
- Data analysis is presented in a fun, sticky-note-style dashboard, supporting visualizations like pie and bar charts for on-the-fly insights.
- “We wanted it to be fun...Girls usually have [blackboards with] sticky notes...So for analysis Dashboard, what we did was we designed the UI to be like sticky note information.” (Nermeen, 06:37)

Target Users and Early Adoption [07:28–10:02]

Datrix targets small and medium retail businesses managing regular purchase and sales transactions, with a roadmap to serve larger enterprises.
Early testers included a construction chemicals business (Nermeen’s father’s company) and a local bakery, both of whom provided enthusiastic feedback and feature requests.
“We had like over 50 people sign up for a wish list, so that was really exciting.” (Nermeen, 09:45)

Tackling Technical Challenges & Finding Magic Moments [11:21–12:51]

The hardest technical hurdle was integrating with Gmail securely and reliably.
Nermeen’s “goosebumps moment” came when the email integration worked and Datrix could process dummy purchase orders accurately—a simple idea with massive economic impact.
“My wow moment was how simple this idea is...when I finally coded the script for the email integration...that’s when I realized it can be huge.” (Nermeen, 11:24)
A client reportedly spent “literally millions per annum on data entry and data automation.” (Nermeen, 12:19)
Driving motivation: making people’s work lives simpler, akin to the everyday revolution of inventing chairs.

Ethical Guardrails and Data Security [12:51–14:48]

Datrix is intentional about not using reinforcement learning to avoid data from one client being used to train a broader model or LLM.
Data is processed purely for extraction and input—no ongoing learning or retention across accounts.
The team is proactively studying compliance standards and international data security certifications.
“We want to make sure not to use any of the reinforcement learning models.” (Nermeen, 13:51)

How Datrix “Thinks”: No Model Training or Data Retention [14:11–14:48]

Datrix ingests a database’s schema (“column name”) and uses vector-based matching to pull corresponding fields from documents—pure matching, not ongoing model training.
“It does not train. It does not need to train because its job is to pretty much like find...the column name…it would go to your P.O. and would search for something similar...” (Nermeen, 14:14)

Notable Quotes & Memorable Moments

“In every company, there's a hidden monster. It's called paperwork.” (Dr. Tamara Nall, 00:00)
“My wow moment was how simple this idea is and why did it not exist?...when I finally...coded the script for the email integration...that's when I realized it can be huge.” (Nermeen, 11:22)
“I want to make life simpler generally. Do you know what I mean? Like how chair was invented...I want to have that simplicity that kind of like helps people.” (Nermeen, 12:39)
“We want to make sure not to use any of the reinforcement learning models.” (Nermeen, 13:51)

Key Timestamps

02:33 – Nermeen’s background and the problem that inspired Datrix
04:34 – How Datrix works (technical breakdown and platform design)
07:28 – Target customers and how small businesses use Datrix
08:46 – Early user testing stories and initial feedback
11:21 – Technical hurdles, “goosebumps” moments, and validation
12:51 – Addressing data ethics and compliance
14:11 – Technical details about data processing and privacy

wavePod

Powered by Wave AI

Summary

Summary