How we work
AI does the reading. Engineers do the deciding.
Every consultancy now says it uses AI. Here is specifically what we do with it, where it earns its keep, and where we do not let it near the work. If you have been pitched an automation percentage, ask whoever pitched it these same questions.
Read the estate
We point AI tooling at the exported mappings, workflows and job definitions and get back a dependency graph, a difficulty classification per object, and documentation for an estate that has not been documented since the people who built it left. This is where the time saving is real and where even the sceptics agree it is real — the burden being removed is processing volume, not judgement.
Convert with a human on every artifact
AI drafts the conversion. A named senior engineer reviews every single artifact before it moves. We do not publish an automation percentage, because we have not measured yours — and anyone quoting you one before seeing your estate is quoting you a marketing number.
Prove it with parity tests
The question that matters is whether the new pipeline produces the same rows as the old one. We generate the comparison suite as part of conversion and run it every wave. This is the answer to the only objection that should worry you about AI-assisted migration: nobody has to trust the AI, because the output is tested against the system it replaces.
Ship through CI/CD
Every artifact deploys through Azure DevOps pipelines across environments. Versioned, reviewed, reproducible, and rolled back if it misbehaves. The same discipline you would apply to application code, applied to integration code.
What we will not tell you
- That AI replaces the engineer. It does not, and a firm that says otherwise will hand you converted code nobody has read.
- An automation percentage for an estate we have not seen.
- That we do everything. We name what we do not do, in writing, before you sign.