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Cloud migration for a SaaS platform

A monolith on ageing servers made every release risky, slow, and hard to scale.

Sector
SaaS Platform
Engagement
Scale & Advise
Stack
AWS · Docker · CI/CD

The challenge

An established SaaS platform was running a monolith on servers that were increasingly expensive to keep alive. Deploys were manual and nerve-wracking, downtime was creeping up, and scaling for growth simply wasn't realistic on the old stack — but a full rewrite was too risky to take on all at once.

The process

We mapped the system and the riskiest parts of the release process first, then agreed a migration plan with clear milestones — modernising incrementally rather than betting the business on a big-bang cutover. We containerised and moved services in stages, put monitoring and automated rollbacks in place before each step, and kept the team shipping throughout so the migration never froze the roadmap.

What we shipped

  • A containerised application on managed cloud infrastructure
  • A CI/CD pipeline with monitoring and automated rollbacks
  • Infrastructure as code — versioned and repeatable
  • An incremental migration, with no risky big-bang rewrite

The impact

  • 99.9% uptime after migration
  • 3× faster, fully automated deploys
  • Zero big-bang rewrites required
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More work

A few more problems we've solved.

Project matching dashboard and system architecture overview

We helped a consulting agency match the right person to the right project, instantly

Automatically matching consultants to assignments — with a clear, justified recommendation.

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A redesigned fintech trading dashboard

A European fintech dashboard

Their trading dashboard was slow, hard to extend, and losing users to faster competitors.

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An AI copilot drafting a support reply

An AI support copilot

Support agents were drowning in repetitive tickets and slow to find the right answer.

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An AI assistant answering a question with a cited source

Discover how we turned scattered banking documentation into instant, trustworthy answers

Teams spent hours hunting for the right answer across hundreds of procedures and regulatory documents.

View case study →
A dashboard tracking an AI assistant’s quality over time

We built the quality checks that keep an AI assistant trustworthy over time

An AI assistant already in daily use risked losing quality with every update, with no way to catch it.

View case study →
Nodus

A product studio. We design, build, and ship AI-native products — end to end.

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