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EndorsedendorsedOngoing

Microservices Migration & AI Product Development for Fabriq

Services Covered:

Custom Software Development • AI and Machine Learning
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Industry
Industry

Information Technology

Duration
Durationinfo-circle

9 months

Budget
Budget

Confidential

Start Date
Start Date

1 July 2025

Team Size
Team Size

1-5

Engagement Model
Engagement Model

Nearshore

Project Capability Score (PCS)

gauge meter
83/100
Strong

Project Capability Score (PCS) estimates how capable Django Stars is of successfully delivering a project like this, based on its past experience, track record, and ability to handle similar work. Learn more about PCS

Client

Fabriq

mapStavanger, Norway

Project Summary

Django Stars provided software development services to Fabriq, an AI-products company, migrating a monolithic Django app to microservices, building frontend UI/UX, and implementing CI/CD. The engagement covered multiple AI-enabled projects — including a clinical decision support system deployed to 28 US hospitals, a personalised fan engagement solution for Major League Baseball, and a research assistant for a geoscience publisher — all leveraging Fabriq's orchestration framework. Django Stars integrated with the client's teams and consistently met deliverables on time and on budget while communicating via Jira, Slack, and Teams.

Key Challenges

  • Move from a monolithic Django application to microservices
  • Implement DevOps and CI/CD to increase deployment speed
  • Build a distributed, event-driven architecture for an agentic AI platform
  • Design and deliver frontend UI and UX
  • Provide client-facing engineers as forward-deployed team members

Project Deliverables

  • Data transformation
  • Agentic harnesses
  • Traces and logging
  • Event-driven architecture
  • UI and UX
  • CI/CD pipelines
  • Integration with customer delivery teams

Project Solution

Django Stars was engaged to migrate a monolithic Django application to microservices, implement DevOps practices including CI/CD, and build frontend UI/UX while providing client-facing engineering support. The vendor assigned a small cross-functional team (2–5 employees) to deliver three AI-enabled projects — a clinical decision support system, a personalised fan engagement solution, and a research assistant — all leveraging Fabriq's orchestration and execution framework. Deliverables included data transformation, agentic harnesses, tracing and logging, an event-driven architecture, UI/UX work, CI/CD pipelines, and integrated collaboration with the client's delivery teams.

Project Outcome

  • Deliverables met on time and on budget
  • Django Stars integrated effectively with the client's team and participated in client-facing work
  • Communication was strong via Jira, Slack, and Microsoft Teams
  • The offering scaled from a small initial engagement to a growing collaboration over time

Platforms

  • AI/ML PlatformAI/ML Platform
  • WebWeb

Tech Stack

  • DjangoDjango
  • PythonPython

Client Endorsement

Overall Review Rating

5star

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

“Django Stars has met all the deliverables on time and within the budget. The team has integrated well with the client's team and has joined them in client-facing work. They have also demonstrated exceptional communication skills through Jira, Slack, and Microsoft Teams.”

Jesse Lord

Co-Founder

Note: This endorsement is based on publicly available client feedback from external review sources.

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