Modernisation | Effektiv — AU AI services

11 wks

median delivery, compared to 14–18 months on a Big-4 quote

60%

lower cost on the same scope, saving passed to you not us

undocumented fields found in week one on an ASX-200 modernisation

100%

of code human-reviewed — every line attributable to an agent, prompt, and model

The core problem

The problem was never the code. It was that nobody went to the data.

Every failed modernisation project has the same archaeology problem. The documentation describes the system as it was designed — not as it runs. The developers who understood the hardest corners left years before the rebuild began. Migration scripts were written by hand from that incomplete picture and failed at cut-over because fields the documentation never mentioned were silently load-bearing.

An ASX-200 manufacturer had tried the rebuild twice. Every prior attempt stalled at data migration. Effektiv's Diagnose phase found twenty-three undocumented fields in the first week — by reading the data, not the spec. Eleven weeks later the new system was in production at $560K against a $1.4M Big-4 quote.

What changes

The same challenge. Two very different outcomes.

Without Effektiv

14–18 month rebuild on a Big-4 quote
Documentation-led discovery missing 10–30 fields
Migration scripts authored by hand from incomplete specs
Cut-over rehearsed once, often on the night
$1.4M average cost on comparable engagements
Vendor owns the rig and the team at exit

With Effektiv

9–14 week rebuild with AI agents reading the data
Data-led discovery surfaces every load-bearing field in week one
Migration scripts written, run, corrected, and re-run by AI agents
Cut-over rehearsed three times against a model copy of your stack
$560K median cost on outcome-priced engagements
Your team owns the rig, trace database, and runbook at exit

How we deliver

Diagnose. Design. Deliver.

Two weeks of listening before a line of code. The price is fixed at the end of Design — not at kick-off.

Phase 1 · 1–2 weeks

Diagnose

AI agents read the full codebase and map the flows. We audit what the documentation says versus what the code and data actually do. The twenty-three-field problem at the ASX-200 manufacturer was found here — in the data, not at cut-over.

Phase 2 · 1–2 weeks

Design

A rebuild plan shaped to what Diagnose found, the cut-over strategy, and the outcome contract. The rollback plan is specified here — not improvised on the night. The price agreed at the end of Design is the price on the invoice.

Phase 3 · 9–14 weeks

Deliver

Migration scripts written and re-run by AI agents against a model copy of your stack. Senior pair reviews every step against the eval rig. The cut-over is rehearsed in the model copy before any production data moves. Your team owns the system at exit.

What you walk away with

Everything ships to your team at exit. No lock-in.

🛠

New system in production

Cut over by week 11 median, rollback path tested, runbook in your repo. Your team ran the production cut.

🧪

Eval rig source code

Schema-match, regression-delta, cut-over rehearsal, rollback timing, row integrity. Runnable code, yours at exit.

🗄

Authorship trace database

Every line attributable to an agent, prompt, and model version. Audit-ready for regulated clients.

📒

Cut-over runbook

Three rehearsals against a model copy. Rollback gates defined. Your team ran the production cut-over.

🎓

A team that runs it

Senior pair embedded through Deliver. Knowledge transfer woven in through the build, not workshopped at exit.

Quality gates

What the eval rig measures.

Every output passes a multi-gate evaluation before it merges or ships. Outputs that fail do not proceed. The eval rig and all gate code are yours at exit.

Schema match across every migrated table — threshold 99.5% before any milestone closes
Regression delta against the existing system on a reference set the client provides
Cut-over rehearsal pass count — target three consecutive clean runs against the model copy
Rollback timing — target under 15 minutes from decision to restored state
Row-level data integrity on the model copy, with diff samples reviewed by the client team

Eval rig · sample run

Schema match across every migrated table —PASS
Regression delta against the existing system on PASS
Cut-over rehearsal pass count — target three conPASS
Rollback timing — target under 15 minutes from dPASS
Row-level data integrity on the model copyPASS

Eval rig source code shipped to your repo at exit.

Common questions

Legacy replacement at AI pace. 40–60% lower cost.

The problem was never the code. It was that nobody went to the data.

The same challenge. Two very different outcomes.

Diagnose. Design. Deliver.

Diagnose

Design

Deliver

Everything ships to your team at exit. No lock-in.

New system in production

Eval rig source code

Authorship trace database

Cut-over runbook

A team that runs it

What the eval rig measures.

Other ways we work with you.

Operations & Integration

AI Adoption

Software Build

Maintenance & SRE

Customer Experience

Frequently asked questions.

How long does a modernisation engagement actually take?

How does the Diagnose Engine find undocumented rules in legacy code?

Will my team need to be involved throughout the engagement?

What happens if the build misses the agreed outcome?

Can you replace a system that is still running in production?

Do you support NetSuite, SAP, Dynamics and other vendor platforms?

See what your modernisation costs at Effektiv pace.