If your platform was built between 2010 and 2020, still runs the business, and the people who built it are gone — Aleph Engineering wraps it in an AI-assisted engineering harness so change gets cheaper, upgrades become possible again, and the system stops depending on one person's memory. Evidence first. No fixed-deadline promises.
Whoever understood the system left years ago. What's left is code, a production database, and institutional knowledge that walked out the door.
No tests, thin logs, unclear blast radius. Teams stop touching what they don't understand — and features stop shipping.
Without a map of the real risk, a rewrite is a leap of faith — so the platform keeps aging while the business depends on it more, not less.
Fewer fire-fights, less senior time burned re-deriving what the system does, and changes that used to take weeks because the behavior is finally documented and tested.
Once you can see the system, you can safely ship features, connect modern tooling and AI, and respond to the business again — instead of protecting the platform from ever being touched.
Documentation, tests, and an incremental migration plan mean the system can keep evolving for the next decade, instead of surviving only until it can't.
The context that used to live in one person's head now lives in a handbook, code maps, and agent-ready documentation — a new hire, or an AI agent, can pick it up fast.
Leadership finally sees where the real risk and cost sit, so modernization gets funded and scoped with data — not postponed indefinitely out of uncertainty.
Where danger actually lives, ranked by evidence gathered from the running system — not assumption.
Every service, dependency, and data flow made explicit and current, not left to tribal memory.
Business capabilities reconstructed from the code that actually runs today, not the diagram from 2019.
Characterization tests and monitoring so any future change is measurable, not a leap of faith.
A target architecture staged into waves, each one shippable and each one reversible.
ADRs, code maps, and agent-ready context — built so a new engineer is productive within a day.
Ranked from most to least critical in our hiring bar:
The language and framework are rarely the hard part — anyone can hire for PHP or Node. What's scarce is the AI-native practice around them: understanding a system nobody documented, engineering the harness that makes change measurable, and turning what we learn into something that keeps working after we leave.
Proven across the stacks legacy systems actually run on — PHP, Python, Node.js, React, Flutter, Odoo, mobile, and serverless — without that being the point.
Roughly two working weeks, on site or remote, scoped to the access you're comfortable granting. It ends with a decision either of us can make with real information — not a sales deck.