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Launching a product is exciting—watching your backend crash under real traffic is not.When your CRM, payments, databases, and team alerts don't talk to each other, your business loses dozens of productive hours every week. We build real-time REST and GraphQL data pipelines with automated webhook retry logic to keep your entire stack in perfect harmony.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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If you run an agency or a B2B business, you know that finding qualified leads takes hours of manual work every single day. I had this exact problem, so I built an automated system to solve it.
I engineered an intent discovery pipeline using n8n and the DeepSeek API. It runs on a self-hosted VPS, pulls real-time data from platforms like Reddit and GitHub, scores the intent of every post using AI, and pings my phone with only the high-quality leads.
It completely eliminated manual prospecting for my own business.
I’m sharing the open-source repo with anyone who wants to see the code—just shoot me a DM.
But if you want a custom, fully integrated version of this built for your sales team without having to touch a single line of code, this is exactly what I do. I build robust n8n automations and AI agents that save hours of manual operations.
Commodity Trade Reporting Application | Java, Spring Boot, SQL, Kafka
Worked as a Java Developer and Project Lead , leading the development of a reporting application for commodity trade data used by large traders and supporting London Metal Exchange (LME)-related reporting requirements.
Key responsibilities included designing and developing backend services using Java and Spring Boot, implementing REST APIs and business logic, working with SQL databases for trade data processing and reporting, and using Apache Kafka for reliable event-driven data communication.
As a project lead, I was responsible for coordinating development activities, understanding business requirements, designing technical solutions, reviewing code, troubleshooting issues, and ensuring the application met functional and technical requirements.
Technologies: Java, Spring Boot, SQL, Apache Kafka, REST APIs, Maven, Git, JUnit, Mockito.
Does someone on your team lose a day or two building the sales report every week or month?
That was the starting point at Mokobara, the premium travel brand. Pulling sales across all their stores, checking returns, reconciling the numbers and emailing each manager took 1 to 2 days every cycle, with 8+ hours of that spent just reconciling.
I built them an automation on Make.com a few months ago. It has run on its own ever since, weekly and monthly:
Make pulls every sales record from BigQuery, 100K to 250K per cycle. That is too many for one request, so it reads them page by page and stitches them back together.
It calculates the numbers the team actually uses: net sales per store after returns and discounts, return rates and the change against the last period.
It builds a CSV with the full breakdown and a short email summary you can read in 60 seconds.
It reads a Google Sheet of store representatives and emails every one of them the report for their own store.
The result: the reporting problem is gone. The report went from 1 to 2 days of manual work to fully automatic, and the 8+ hours of reconciliation dropped to zero.
Two things I would do the same way again:
Calculate "net sales" in the automation, not in the warehouse. The business rule for what counts as a net sale is not what the raw data stores.
Send people their slice, not the whole report. One report for everyone gets skimmed by everyone.
The figures in the image are placeholders, the real ones stay with the client.
What report is your team still building by hand? Tell me where the data lives and I will tell you how I would automate it.
