Data labeling and crowdsourcing platforms offer services for AI and machine learning development. Setronica partnered with a leading AI crowdsourcing platform to enhance Large Language Models (LLMs) through human-guided data processing.
Let’s see how our team of developers delivers high-quality inputs for AI model training while overcoming significant operational challenges.
Our client specializes in data labeling and processing services for artificial intelligence and machine learning development. They create task pools and recruit human contributors who provide critical inputs for LLM enhancement. Through cross-validation and quality control measures, they deliver reliable results to AI developers.
As one of the key partners for the platform, Setronica is responsible for:
Our work focuses primarily on helping developers provide direct assistance rather than generic search-like responses, with expertise in languages including:
Primary focus: Python, Java, JavaScript, TypeScript, C++, C, C#
Secondary focus: SQL (advanced), PHP, Go, Kotlin, CSS/HTML, Angular, React, node.js
At Setronica, we implement Agile methodologies with a practical focus on clear communication and adaptability.
Our workflow includes daily stand-ups and weekly review calls where team members share their progress and plans. This regular schedule helps us stay coordinated and manage client expectations effectively.
When requirements change, we quickly adjust our approach. We maintain dedicated channels to track instruction updates and create shared spaces where team members can discuss complex issues and agree on solutions for unusual situations.
The initial phases of the project tested our team’s adaptability in several ways.
Client environment had frequent requirement changes – sometimes multiple times per week. This created a challenging workflow where specifications would shift just as we were implementing previous versions.
Communication gaps added another layer of difficulty. Feedback cycles stretched longer than expected, and clarifications to technical questions often faced significant delays. Meanwhile, we still needed to meet original deadlines, despite receiving data batches later than planned.
These conditions occasionally forced our team to work extended hours to deliver on commitments. Rather than simply accepting these difficulties, we worked with the client to develop practical solutions:
This experience reinforced our belief that even difficult projects can succeed with the right process adjustments and open communication.
Our client partnership improved significantly when they began actively implementing our feedback. This responsiveness created tangible workflow improvements for both teams.
Based on our suggestions, the client introduced three practical changes:
These simple adjustments made daily operations more efficient and reduced confusion.
As the project progressed, we built credibility in three key ways:
These practices helped transform an initially challenging project into a productive long-term relationship based on reliability and practical solutions.
And here’s what our client says about Setronica:
“We appreciate working with you for your high level of professionalism. You are not afraid of new challenges, and our experiments on new tasks help us scale the business faster. But most importantly, we succeed in building true partnership relationships through dialogue. Our requirements often change, and I am personally grateful that you not only accommodate and adapt to them, but also suggest improvements.”
This project taught us valuable lessons about adapting to new challenges. Despite having no previous experience with AI data labeling work, our team quickly learned the client’s requirements and built an effective workflow.
The initial difficulties pushed us to develop better processes and communication methods. Over time, these improvements helped us handle increasingly complex tasks.
As we demonstrated reliability, the client assigned us more advanced work. This created learning opportunities for our team members and allowed us to expand our technical capabilities in new directions.
The experience showed us that with the right approach, teams can successfully adapt to unfamiliar project types and gradually build expertise in new technical domains.
