The engine worked. The product did not exist yet.
Purgo's AI agents could write production-grade data pipelines. What they had no way to do was reach a user. We built the platform around them.
15 minutes to build a data pipeline, down from 10+ days
The problem
Purgo began as an acquisition: a solution with the foundations for analyzing code with AI, and a CEO, Sang Kim, who could see a larger product in it. The ambition was multi-agent AI doing the work data engineers do by hand, and the target was one of the least forgiving spaces in the field, where output that is merely plausible is worse than no output at all.
The intended experience was specific. A user writes a requirement in Jira. What comes back is production-ready code that respects business logic, enterprise rules, and QA standards.
The gap was not the intelligence. It was everything around it. The original infrastructure had been built to prove a concept, not to carry a SaaS product, and an engine with no console, no integrations, no QA pipeline, and no interface is not something a customer can buy.
What we built
The product experience, and the plumbing underneath it. A web application, a console, and a dashboard for the AI's functionality to surface through. A Jira integration so a development task could be submitted and its generated code returned against that case. A GitHub integration so outputs are documented and stored automatically rather than by hand.
Then the part that decides whether an AI product is trusted twice: a QA and testing pipeline that validates not only the interface but the behavior of the intelligent workflows themselves. An AI feature without a way to check its output is a hope, not a feature.
We also carried the documentation, the UI/UX work, and the marketing site materials, so the launch was a product launch rather than a demo with a landing page.
- Web application, console, and dashboard
- Jira integration for task submission and code retrieval
- GitHub integration for automatic documentation and storage
- QA and testing pipelines covering the intelligent workflows, not just the UI
- Documentation, UI/UX, and launch materials
How it works
Step 01
The requirement goes into Jira
A user writes a ticket in the tool their team already runs on. No new workflow to adopt.
Step 02
The platform picks it up
The console and dashboard hand the request to Purgo's AI agents with the project's context attached.
Step 03
Agents generate the code
Production-ready pipeline code, written against business logic, enterprise rules, and QA standards.
Step 04
QA validates, GitHub records
The output runs through the testing pipeline, then lands documented and stored in the repository.
Step 05
Reviewed and deployed
A human reviews code that arrives standardized, maintainable, and readable enough to debug.
From design to console and testing the overall system, Koombea has helped with almost every aspect of getting the platform off the ground.
What happened
The source case study publishes no revenue or company-size figures. The one number Purgo does publish is the one that matters to their buyer.
- Pipeline creation cut from over 10 days to under 15 minutes, Purgo's published figure
- Requirements entered in Jira return production-ready code
- Validation, documentation, and deployment run without manual bottlenecks
- Output that is standardized and maintainable, and therefore easier to debug than hand-rolled equivalents
Delivery notes
Stack: Python, web application and console, Jira API, GitHub API, QA and testing pipelines.
The engagement ran across time zones against a distributed architecture, with backend systems and data flows aligned between teams. Sang singled out onboarding: as the team scaled and changed, new people arrived productive, which is a documentation and process outcome rather than a staffing one.
Capabilities applied: Product Engineering, Integrations and Data. Delivered as a committed scope, estimated in story points and priced before work began.
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