Case study

EquippedAI: reducing cloud cost by 75% while stabilizing a complex platform.

CoEdify deployed four developers into a 3-year engagement with EquippedAI, now part of Belasko UK. The work stabilized an unstable product suite, integrated AI workflows into Minerva, and rebuilt operational infrastructure to run more efficiently.

Relationship

3-year engagement ending January 2026

Team

4 CoEdify developers deployed

Outcome

Azure infrastructure cost reduced by 75%

A product suite that needed stability, AI workflow integration, and a more efficient operating base.

EquippedAI's platform work was not a narrow optimization task. The engagement covered product stabilization, AI workflow integration across Minerva, and infrastructure changes that reduced recurring cloud waste.

The important lesson is that the cost reduction was not a standalone billing trick. It came from improving the architecture and operating model around the product suite.

Engineering work across product, AI workflows, and infrastructure.

Delivery scope

  • Stabilized an unstable private-equity management product suite
  • Integrated AI workflows into EquippedAI's core Minerva platform
  • Rebuilt operational infrastructure so the platform could run more efficiently
  • Reduced operational overhead while maintaining the same output

Operational change

  • Reduced monthly cloud infrastructure cost from around Rs40L/month to Rs10L/month
  • Simplified repeated infrastructure and service patterns around the product suite
  • Kept the focus on architecture and operating efficiency rather than treating cloud cost as a billing-only problem

AI delivery still depends on disciplined platform engineering.

The EquippedAI work is useful proof because it was not a demo or a strategy document. It was sustained production engineering inside a complex product environment.

That is the same standard CoEdify brings to agentic workflows, AI product features, and automation work: ship working systems, keep the operating model visible, and make the result easier to run.

Bring the workflow or platform problem you need shipped.

Start with a short engineering call. If there is a fit, the first phase is scoped to produce a meaningful deliverable within two weeks.

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