How Brick replaced ad-hoc data ingestion with a data-driven operation
How Brick replaced ad-hoc data ingestion with a data-driven operation
Brick centralized their data ingestion with Erathos and now manages their entire data stack with a single analyst, eliminating the need for a dedicated data engineering team for pipelines.
2 to 3 days
2 to 3 days
to deploy a new data source (previously 2 to 3 months)
7 mins
7 mins
to generate the monthly Investor Report (which previously took an entire morning)
1 data analyst
1 data analyst
supports the company's entire data infrastructure
About:
Brick is the native AI operating system for insurance risk decisions, empowering business teams to build, adjust, and audit underwriting, claims, and fraud prevention workflows without relying on IT.
Location:
Curitiba, Paraná
Industry:
SaaS / Insurtech
Employees:
25
Erathos clients since:
October 2025
Connected sources
Brick is the AI-native operating system for insurance risk decisions, used by underwriting, claims, and fraud prevention teams. Before Erathos, data ingestion was manually maintained by the company's co-founder, with Airflow orchestrating extractions and DBT running on top of it with no guarantee that the data was up to date. Any schema change in an API would break the pipeline, and resolving this consumed a real portion of the time of those who should have been building the product. After centralizing ingestion with Erathos, Brick started maintaining its entire data stack with a single analyst, cutting the time to deploy a new data source from months to days, while supporting product, operations, and finance decisions with reliable data.
The Challenge
Before Erathos, Brick handled their own data ingestion using Airflow to trigger routines that dumped data into Cloud Storage and then into a delta table in BigQuery, with dbt running on top regardless of whether the data was actually updated. Carlos Schwabe, co-founder, describes the result as a basic ingestion process set up with the everyday tools of any engineering team: the customer table would update, but the sales table wouldn't, and the final report would end up with a sale tied to a customer who didn't exist.
The biggest cost wasn't running the pipeline; it was maintaining it. Every data source (HubSpot, Conta Azul, Zendesk, production database) had a different API—some documented, some not—and every change in scope, such as needing call data instead of just contacts, forced the team to dive back into the API documentation and rebuild the cursor logic from scratch. About twice a week, Carlos had to stop what he was doing to fix some breakage and ensure the data would reconcile again.
On the financial side, the issue manifested as delayed decision-making. Thiago Paz, CFO, recalls a night when the team stayed in the office until 10:00 PM trying to calculate the churn rate because the data was scattered across the ERP, production database, and CRM, and there was no way to join the information without manually putting it all together in a spreadsheet.
At its core, Brick didn't have a tooling problem; they had a time problem: every hour spent maintaining data pipelines was an hour less spent on product and strategic decisions—exactly at the phase when the company needed velocity the most.
About:
Brick is the native AI operating system for insurance risk decisions, empowering business teams to build, adjust, and audit underwriting, claims, and fraud prevention workflows without relying on IT.
Location:
Curitiba, Paraná
Industry:
SaaS / Insurtech
Employees:
25
Erathos clients since:
October 2025
Connected sources
Brick is the AI-native operating system for insurance risk decisions, used by underwriting, claims, and fraud prevention teams. Before Erathos, data ingestion was manually maintained by the company's co-founder, with Airflow orchestrating extractions and DBT running on top of it with no guarantee that the data was up to date. Any schema change in an API would break the pipeline, and resolving this consumed a real portion of the time of those who should have been building the product. After centralizing ingestion with Erathos, Brick started maintaining its entire data stack with a single analyst, cutting the time to deploy a new data source from months to days, while supporting product, operations, and finance decisions with reliable data.
The Challenge
Before Erathos, Brick handled their own data ingestion using Airflow to trigger routines that dumped data into Cloud Storage and then into a delta table in BigQuery, with dbt running on top regardless of whether the data was actually updated. Carlos Schwabe, co-founder, describes the result as a basic ingestion process set up with the everyday tools of any engineering team: the customer table would update, but the sales table wouldn't, and the final report would end up with a sale tied to a customer who didn't exist.
The biggest cost wasn't running the pipeline; it was maintaining it. Every data source (HubSpot, Conta Azul, Zendesk, production database) had a different API—some documented, some not—and every change in scope, such as needing call data instead of just contacts, forced the team to dive back into the API documentation and rebuild the cursor logic from scratch. About twice a week, Carlos had to stop what he was doing to fix some breakage and ensure the data would reconcile again.
On the financial side, the issue manifested as delayed decision-making. Thiago Paz, CFO, recalls a night when the team stayed in the office until 10:00 PM trying to calculate the churn rate because the data was scattered across the ERP, production database, and CRM, and there was no way to join the information without manually putting it all together in a spreadsheet.
At its core, Brick didn't have a tooling problem; they had a time problem: every hour spent maintaining data pipelines was an hour less spent on product and strategic decisions—exactly at the phase when the company needed velocity the most.
Brick is the AI-native operating system for insurance risk decisions, used by underwriting, claims, and fraud prevention teams. Before Erathos, data ingestion was manually maintained by the company's co-founder, with Airflow orchestrating extractions and DBT running on top of it with no guarantee that the data was up to date. Any schema change in an API would break the pipeline, and resolving this consumed a real portion of the time of those who should have been building the product. After centralizing ingestion with Erathos, Brick started maintaining its entire data stack with a single analyst, cutting the time to deploy a new data source from months to days, while supporting product, operations, and finance decisions with reliable data.
About:
Brick is the native AI operating system for insurance risk decisions, empowering business teams to build, adjust, and audit underwriting, claims, and fraud prevention workflows without relying on IT.
Location:
Curitiba, Paraná
Industry:
SaaS / Insurtech
Employees:
25
Erathos clients since:
October 2025
Connected sources
The Solution
Brick found out about Erathos through a referral. Carlos wanted a solution that was fast and within budget, with no time to deeply evaluate multiple alternatives. As soon as the first connection was implemented, the difference was clear: instead of mapping endpoints and maintaining custom scripts, they just had to plug in the connector for the data to arrive up-to-date, while Carlos continued to maintain on his own the sources that Erathos did not yet cover at that time.
This transition phase made it obvious to the team how data ingestion via plug-and-play, ready-to-use connectors was simpler and more reliable than maintaining their own pipelines line by line. With every new source Erathos added to its catalog, Brick eliminated another point of internal maintenance, to the point where, according to Thiago, there is currently no system used by the company without data available via Erathos.
The Challenge
Before Erathos, Brick handled their own data ingestion using Airflow to trigger routines that dumped data into Cloud Storage and then into a delta table in BigQuery, with dbt running on top regardless of whether the data was actually updated. Carlos Schwabe, co-founder, describes the result as a basic ingestion process set up with the everyday tools of any engineering team: the customer table would update, but the sales table wouldn't, and the final report would end up with a sale tied to a customer who didn't exist.
The biggest cost wasn't running the pipeline; it was maintaining it. Every data source (HubSpot, Conta Azul, Zendesk, production database) had a different API—some documented, some not—and every change in scope, such as needing call data instead of just contacts, forced the team to dive back into the API documentation and rebuild the cursor logic from scratch. About twice a week, Carlos had to stop what he was doing to fix some breakage and ensure the data would reconcile again.
On the financial side, the issue manifested as delayed decision-making. Thiago Paz, CFO, recalls a night when the team stayed in the office until 10:00 PM trying to calculate the churn rate because the data was scattered across the ERP, production database, and CRM, and there was no way to join the information without manually putting it all together in a spreadsheet.
At its core, Brick didn't have a tooling problem; they had a time problem: every hour spent maintaining data pipelines was an hour less spent on product and strategic decisions—exactly at the phase when the company needed velocity the most.
The Solution
Brick found out about Erathos through a referral. Carlos wanted a solution that was fast and within budget, with no time to deeply evaluate multiple alternatives. As soon as the first connection was implemented, the difference was clear: instead of mapping endpoints and maintaining custom scripts, they just had to plug in the connector for the data to arrive up-to-date, while Carlos continued to maintain on his own the sources that Erathos did not yet cover at that time.
This transition phase made it obvious to the team how data ingestion via plug-and-play, ready-to-use connectors was simpler and more reliable than maintaining their own pipelines line by line. With every new source Erathos added to its catalog, Brick eliminated another point of internal maintenance, to the point where, according to Thiago, there is currently no system used by the company without data available via Erathos.
We used to have a lot of rework, and now it's gone. Working this way is much more efficient. If it's not your core business, just hire Erathos. Spend your time modeling your own domain, not someone else's.
Carlos Schwabe
Co-Founder & CDO
The Results
Time to implement a new data source dropped from 2 to 3 months to just 2 to 3 days using Erathos, according to Bruno Rosso.
The monthly Investor Report, which used to take up a whole morning's work, now takes only 7 minutes.
Monthly financial closing went from a day and a half to a single morning.
Calculating metrics like churn, which once required a full night of manual work, is now available for query and updates at any time, with no manual effort needed.
Carlos, co-founder, went from spending 4 to 5 hours a week maintaining data pipelines to spending zero.
The company's entire data infrastructure is now maintained with a much leaner investment, supported by a single data analyst.
More than just saving time, these numbers reflect Brick's initial goal: building a truly data-driven culture, where product, operations, and finance decisions are backed by reliable data accessible to the entire team, rather than restricted to a single technical person.
Each new source implementation, if I had to do it on my own, would take about two to three months. With Erathos, it's done in a few hours. I don't need the skills of a data engineer to deliver real value with the company’s data.
Bruno Rosso
Data Analyst
O retorno sobre o investimento (ROI)
O time da Brick avaliou que, sem a Erathos, seria necessário montar um time interno de 2 a 3 pessoas entre engenheiros de dados e analistas de dados para sustentar a mesma operação. Usando salários médios de mercado no Brasil (R$8.000 para engenheiro de dados e R$5.000 para analista de dados, mais encargos CLT), o custo estimado de um time de 2 engenheiros e 1 analistas dedicados seria de R$793.800 no período.
Custo evitado nos últimos 21 meses (2 engenheiros de dados + 1 analistas de dados, salários médios de mercado + encargos CLT): R$783.751
R$ 783.751 de economia líquida, aproximadamente 78x o valor investido na Erathos
R$ 783.751 de economia líquida, aproximadamente 78x o valor investido na Erathos
If I didn't have this infrastructure, I would need three or four people to do what Erathos does today. I was able to drive real value from my data with a much leaner team than I ever imagined needing. This is at the core of building a data-driven culture: the team only uses data when it is reliable.
Thiago Paz
CFO
The future
Brick continues to explore the potential of the data infrastructure built with Erathos. On the connector side, Conta Simples and PostHog are in beta and should be tested by the team soon. Bruno also indicated interest in digging deeper into the Pro plan, leveraging job retry customization and custom backfills for larger tables, as well as enabling failure alerts via email or Slack—a feature he didn't realize was available.
About:
Brick is the native AI operating system for insurance risk decisions, empowering business teams to build, adjust, and audit underwriting, claims, and fraud prevention workflows without relying on IT.
Location:
Curitiba, Paraná
Industry:
SaaS / Insurtech
Employees:
25
Erathos clients since:
October 2025
Connected sources
Brick is the AI-native operating system for insurance risk decisions, used by underwriting, claims, and fraud prevention teams. Before Erathos, data ingestion was manually maintained by the company's co-founder, with Airflow orchestrating extractions and DBT running on top of it with no guarantee that the data was up to date. Any schema change in an API would break the pipeline, and resolving this consumed a real portion of the time of those who should have been building the product. After centralizing ingestion with Erathos, Brick started maintaining its entire data stack with a single analyst, cutting the time to deploy a new data source from months to days, while supporting product, operations, and finance decisions with reliable data.
The Challenge
Before Erathos, Brick handled their own data ingestion using Airflow to trigger routines that dumped data into Cloud Storage and then into a delta table in BigQuery, with dbt running on top regardless of whether the data was actually updated. Carlos Schwabe, co-founder, describes the result as a basic ingestion process set up with the everyday tools of any engineering team: the customer table would update, but the sales table wouldn't, and the final report would end up with a sale tied to a customer who didn't exist.
The biggest cost wasn't running the pipeline; it was maintaining it. Every data source (HubSpot, Conta Azul, Zendesk, production database) had a different API—some documented, some not—and every change in scope, such as needing call data instead of just contacts, forced the team to dive back into the API documentation and rebuild the cursor logic from scratch. About twice a week, Carlos had to stop what he was doing to fix some breakage and ensure the data would reconcile again.
On the financial side, the issue manifested as delayed decision-making. Thiago Paz, CFO, recalls a night when the team stayed in the office until 10:00 PM trying to calculate the churn rate because the data was scattered across the ERP, production database, and CRM, and there was no way to join the information without manually putting it all together in a spreadsheet.
At its core, Brick didn't have a tooling problem; they had a time problem: every hour spent maintaining data pipelines was an hour less spent on product and strategic decisions—exactly at the phase when the company needed velocity the most.
The Solution
Brick found out about Erathos through a referral. Carlos wanted a solution that was fast and within budget, with no time to deeply evaluate multiple alternatives. As soon as the first connection was implemented, the difference was clear: instead of mapping endpoints and maintaining custom scripts, they just had to plug in the connector for the data to arrive up-to-date, while Carlos continued to maintain on his own the sources that Erathos did not yet cover at that time.
This transition phase made it obvious to the team how data ingestion via plug-and-play, ready-to-use connectors was simpler and more reliable than maintaining their own pipelines line by line. With every new source Erathos added to its catalog, Brick eliminated another point of internal maintenance, to the point where, according to Thiago, there is currently no system used by the company without data available via Erathos.
The Results
Time to implement a new data source dropped from 2 to 3 months to just 2 to 3 days using Erathos, according to Bruno Rosso.
The monthly Investor Report, which used to take up a whole morning's work, now takes only 7 minutes.
Monthly financial closing went from a day and a half to a single morning.
Calculating metrics like churn, which once required a full night of manual work, is now available for query and updates at any time, with no manual effort needed.
Carlos, co-founder, went from spending 4 to 5 hours a week maintaining data pipelines to spending zero.
The company's entire data infrastructure is now maintained with a much leaner investment, supported by a single data analyst.
More than just saving time, these numbers reflect Brick's initial goal: building a truly data-driven culture, where product, operations, and finance decisions are backed by reliable data accessible to the entire team, rather than restricted to a single technical person.
O retorno sobre o investimento (ROI)
O time da Brick avaliou que, sem a Erathos, seria necessário montar um time interno de 2 a 3 pessoas entre engenheiros de dados e analistas de dados para sustentar a mesma operação. Usando salários médios de mercado no Brasil (R$8.000 para engenheiro de dados e R$5.000 para analista de dados, mais encargos CLT), o custo estimado de um time de 2 engenheiros e 1 analistas dedicados seria de R$793.800 no período.
Custo evitado nos últimos 21 meses (2 engenheiros de dados + 1 analistas de dados, salários médios de mercado + encargos CLT): R$783.751
The future
Brick continues to explore the potential of the data infrastructure built with Erathos. On the connector side, Conta Simples and PostHog are in beta and should be tested by the team soon. Bruno also indicated interest in digging deeper into the Pro plan, leveraging job retry customization and custom backfills for larger tables, as well as enabling failure alerts via email or Slack—a feature he didn't realize was available.
If I didn't have this infrastructure, I would need three or four people to do what Erathos does today. I was able to drive real value from my data with a much leaner team than I ever imagined needing. This is at the core of building a data-driven culture: the team only uses data when it is reliable.
Thiago Paz
CFO
We used to have a lot of rework, and now it's gone. Working this way is much more efficient. If it's not your core business, just hire Erathos. Spend your time modeling your own domain, not someone else's.
Carlos Schwabe
Co-Founder & CDO
Each new source implementation, if I had to do it on my own, would take about two to three months. With Erathos, it's done in a few hours. I don't need the skills of a data engineer to deliver real value with the company’s data.
Bruno Rosso
Data Analyst