If your platform has run the business for longer than anyone wants to admit, the people who built it are gone, and every change feels like defusing a bomb — 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.
Leadership finally sees where the real risk and cost sit, so modernization gets funded and scoped with data — not postponed indefinitely out of uncertainty.
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.
A harness — tests, decision history, and an incremental migration plan — means 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 working harness — tests, code maps, and decision history — that a new hire, or an AI agent, can act on immediately.
Not every bottleneck is technical. We diagnose where it really sits — engineering, sales, or customer relations — and act there first, instead of assuming the fix always starts in the codebase.
One well-established way in: replacing or modernizing a legacy system piece by piece while it keeps running, until the old one can be safely retired — engineers call this the Strangler Fig Pattern. It's not the only route to digital transformation, but it's one of the most proven, and AI now makes it faster and safer to execute. What's new is the harness underneath it: the agent scaffolding, runtime, and guardrails that let AI agents and engineers run the cutover safely, in six steps.
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 and data that actually run today, not the diagram nobody's opened in years.
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 — once the harness is built, a new engineer or agent is productive within a day.
The language and framework are rarely the hard part — anyone can hire for PHP or Node. What's scarce is the seniority to understand a system nobody documented, and engineers who've actually mastered the techniques to do it with AI. That combination is what we select and train for.
Proven across the stacks legacy systems actually run on — PHP, Python, Node.js, React, Flutter, Odoo, mobile, and serverless — the stack was never the hard part.
About five days, on site or remote — faster with a dedicated internal counterpart who blends business knowledge and infrastructure access. Scoped to the access you're comfortable granting, though more access means a deeper audit and stronger results. It ends with a decision either of us can make with real information — not a sales deck.
Bring us the system nobody wants to touch. Start with a two-minute diagnostic, not a cold email.