Platform Migration to AI

Move the work you dread onto AI — without a leap of faith.

Migration is where value is trapped: in brittle legacy and ERP workflows nobody enjoys, or in an AI layer running on a model you've outgrown. We move both — with a parallel run, measured parity, and a controlled cutover, so switching over is a decision, not a gamble.

Scope a migration →Start with the free assessment

Two kinds of migration

Replatform the legacy system — or upgrade the model underneath.

They're different jobs with the same discipline: benchmark first, run in parallel, prove it's better, then cut over. Pick the one you're facing.

Replatform legacy & ERP

Our flagship program: replace the brittle legacy and ERP workflows your team dreads with AI-native systems they'll actually want to use. We map the old system, migrate the data, run the two in parallel, and cut over on your signal.

  • Legacy / ERP workflow mapped and replaced
  • Data migration, parallel run, controlled cutover

Fixed project fee · staged to cutover

Model & provider migration

Move an existing AI workload onto a stronger frontier model. We re-tune the prompts, run evals to prove parity or uplift, guard against regressions, and swap the model behind your app cleanly.

  • Prompts re-tuned, evals prove parity or uplift
  • Regression-guarded, clean model swap

Fixed project fee · benchmark to cutover

Why migrations fail — and how we don’t

The risk in a migration is the cutover. So we make the cutover boring.

Most migration horror stories are really cutover horror stories — the big-bang switch that broke something no one could roll back. We benchmark before, run old and new side by side, and prove the new path is better before anyone depends on it.

  • Benchmark before we touch anything — we measure the current system so “better” is a number, not a feeling.
  • Parallel run — old and new operate side by side until the new one has earned the switch.
  • Proven parity or uplift — evals and real output show the new path matches or beats the old before cutover.
  • Controlled, reversible cutover — you switch on evidence and on your signal, with a way back if anything surprises us.
# Migration — staged, not big-bang
kind:
  - legacy / erp replatform
  - model / provider swap
steps:
  - benchmark: measure today
  - migrate: data + prompts
  - parallel: run side by side
  - prove: parity or uplift
  - cutover: on your signal
rollback: a way back, always

How we de-risk it

Benchmark, parallel-run, prove, then cut over.

The same disciplined path whether you're replacing a legacy core or upgrading the model underneath — so the switch is evidenced, not hoped.

Benchmarked
“Better” is measured against today, not asserted
Parallel run
Old and new side by side until new earns the switch
Reversible
You cut over on evidence, with a way back

Where this fits

The build stage — for what you already have.

Migration is a build engagement — assess, build, deploy, enable — for value trapped in systems you already run. Building something genuinely new instead of replacing? That's an internal agentic platform or a customer-facing product. Regulated replatforms lean on our governance and validation work, and managed operations takes it from cutover onward.

Stuck on a system you've outgrown?

Tell us what you're running today — a legacy workflow or an AI layer on the wrong model. We'll scope a staged migration that proves itself before you switch.

Scope a migration →Get your free assessment