AI-Native Engineering Transformation
Your team, rebuilt around AI — without a re-org or a rewrite.
- Timeline
- 90 days for the first team
- Engagement
- Embedded, fixed monthly
- Built for
- Teams of 10–200 engineers
The problem
Nearly every team has the licenses. Most have flat cycle time to show for it. AI amplifies whatever your engineering system already does — including the parts that were already broken.
DORA's 2025 research is blunt about it: AI amplifies existing practices, good and bad. We fix the practice first.
Who this is for
- Teams of 10–200 engineers
- Already paying for Copilot, Cursor or Claude Code
- Leadership asking for numbers you cannot yet produce
Who this is not for
- Teams who want a one-day training session and a certificate
- Organisations not willing to change how code gets reviewed
How it runs
- 01
Measure first
The baseline goes in during week one, before a single tool changes. Without it, every later claim is an anecdote.
- 02
Find the constraint
Requirements, review, tests or environments — one of them is the bottleneck, and adding AI upstream of it changes nothing.
- 03
Rewire one team
New working agreements, a spec-to-test flow, a review policy that assumes AI authorship, and architecture records an agent can actually read.
- 04
Scale the pattern
Enablement, guardrails and internal champions carry it to the rest of the org.
- 05
Hand over the operating model
You keep running it after we leave. That is the deliverable.
What you get
- Engineering baseline and ongoing delivery metrics
- AI usage policy and tool standard
- Spec to test to review workflow
- Agent-readable repo conventions and context packs
- Enablement curriculum for the whole org
- 90-day rollout plan
AI-Native Engineering Transformation: questions we get asked
How long does it take to make an engineering team AI-native?
Ninety days for the first team. The operating model then transfers to the rest of the organisation over the following two quarters, run by your people rather than by us.
We already pay for Copilot and Cursor. Why isn't our delivery faster?
Because tools sit downstream of the actual constraint. If requirements arrive ambiguous, review is a queue, or environments take a day to provision, faster code generation just grows the queue. We find the constraint before we touch the tooling.
How do you measure whether AI is actually helping?
We install a delivery baseline in week one, before any tool changes, and track it at team level throughout. Cohort-level trends only — never individual developer metrics, which corrupt the data and the culture at the same time.
Will this require reorganizing my team?
No. This changes how work flows through your existing team — how requirements are written, how tests are generated, how review works — not who reports to whom.
Delivered in your region.
- GDPR
- UAE PDPL
- ISO 27001 practices
- Data residency in the EU, the UAE, or your own cloud account
APPINE L.L.C-FZ, Dubai
$ appine assess --fixed-fee
Two weeks. Fixed fee. You end with a decision, not a deck.
A system and codebase audit, a risk register ranked by severity, an engineering baseline, and an explicit recommendation — keep, harden, rebuild, or don't do it at all.
If the answer is “don't hire us,” we'll write that down too.