Legacy modernization for startups and SMBs, before support runs out

Legacy modernization is upgrading the frameworks, runtimes, databases and infrastructure under an existing product so it stays supported, secure and fast, without rebuilding it from scratch. In practice that means version upgrades, dependency cleanup, database and platform migrations, and fixing performance and cloud cost along the way, usually in stages so the product keeps running throughout.

Not for long. Django 4.2 LTS stopped receiving security fixes in April 2026 and Python 3.10 reaches end of life in October 2026, so any vulnerability found after those dates stays open in your stack. The app keeps running, but the packages you depend on gradually drop support for old releases, which makes every later upgrade harder. Plan the move now: Django 5.2 LTS is supported until April 2028.

Plan the move now. Amazon Linux 2 reached end of support in June 2026, and Amazon RDS for MySQL 8.0 reached the end of standard support in July 2026, after which instances still on it move to paid Extended Support. We upgrade the operating system, machine images and database engine versions in stages, testing your application on the new platform before switching traffic.

It is a fixed-scope first step with three parts. We audit every framework, runtime and dependency against its end-of-life date, lay out an upgrade path that calls out where tests are missing and where the risks sit, and review your cloud bill and slowest pages for cost and speed fixes. You come away with a written plan and a fixed estimate for the upgrade, so you can decide what to do and in what order before committing to the work.

By keeping each change small and reversible. Tests go in first so regressions are caught before release, the upgrade ships in stages rather than as one big switch, and the data migration and cutover are rehearsed in staging with a tested rollback plan. Most steps ship like any normal release. Where a cutover needs a maintenance window, we schedule it for your quietest hours and keep it as short as possible.

Yes. The upgrade already touches every layer, so it is the right moment, and the Upgrade Assessment includes a cost and speed review. We profile slow pages and queries (N+1 queries, missing indexes, missing caching), check Core Web Vitals, and look for oversized or idle cloud resources. For teams already running AI features, we also cut LLM and inference cost with caching and model routing.

Yes, for the mechanical work. AI-assisted tooling speeds up syntax changes, deprecation fixes and first drafts of tests, and an engineer reviews every change. Our engineers own the parts where upgrades actually fail: test coverage, data migration and the cutover itself. AI makes the repetitive work faster; it does not decide what is safe to ship.

It depends on scope: how many versions you are behind, how many third-party packages need replacing, and how much test coverage exists today. A small app one LTS release behind is a very different job from a large codebase several versions back with thin tests. The Upgrade Assessment answers this for your system, and you get a fixed estimate for the upgrade after it.

Yes. You own all source code, tests, infrastructure configuration and documentation we produce. Everything is committed to your repositories as we work, with no lock-in, so you can run, extend or bring the work in-house at any time.