What it focuses on
- Startup quality and first-session reliability
- Monetization-sensitive execution and traffic protection
- SDK, plugin, and native integration risk
- Review depth, release readiness, and feature delivery speed
Ship faster, protect revenue-sensitive flows, and raise Unity engineering quality without scaling chaos.
A premium AI transformation offer for Unity mobile founders and CTOs who need a safer startup path, faster feature delivery, stronger release confidence, and less dependence on hero-driven senior review.
Built for founder / CTO buyers, not AI tourists
15 years in mobile development
Playtika 2017-2025
Current architect-level work in GameStory / Apperfun
3 years using AI directly in delivery work
This is not generic AI advisory.
AIRoot is not a prompt pack, a chatbot rollout, or a vague AI strategy exercise. It is a Unity mobile AI operating model for the workflows that matter most.
Best fit
For teams that already feel delivery pressure
- Founders and CEOs who want stronger execution without bloating headcount
- CTOs and engineering leaders who need a practical AI operating model
- Product leaders who want faster feature movement and clearer rollout-risk visibility
- Unity teams dealing with startup issues, SDK churn, plugin risk, and inconsistent review quality
What founders and CTOs are really buying
Not more AI activity. Not more dashboards. Not a generic platform rollout. The actual purchase is a controlled operating upgrade around the Unity workflows that already affect speed, quality, and money.
What this is not for
This is not a fit for tiny teams without budget, broad AI curiosity programs, or companies that want a generic enablement workshop without a live workflow worth transforming.
Where the program creates the most value
This offer is strongest where business pain and engineering pain overlap.
Feature delivery is too slow
Product and engineering spend too much time in back-and-forth before work becomes implementation-ready. Senior judgment is trapped in a few people, and review quality is inconsistent.
Startup friction hurts retention
Users drop during login, loading, consent, offline states, or startup sequencing before they ever reach the core product.
Revenue-sensitive flows are too fragile
Ads, monetization timing, network-quality checks, and startup logic interact in ways that can quietly waste traffic and damage first-session revenue.
SDK and plugin risk is too high
Native bridges, plugins, third-party SDKs, and lifecycle complexity create hard-to-review change surfaces with expensive failure modes.
AI inside the company is fragmented
People are experimenting with AI, but there is no coherent operating model, no governance, and no repeatable workflow that actually improves delivery.
Business outcome
You are not buying a tool. You are buying a controlled operating upgrade.
- Faster feature framing and implementation readiness
- Stronger code review and issue detection before release
- Safer startup, monetization, and SDK change surfaces
- Lower reliance on hero-driven senior intervention
- A repeatable AI operating model the team can keep using
The core domains we transform first
The program starts where workflow redesign can produce fast, measurable leverage.
Delivery acceleration
Move from feature request to implementation-ready plan faster, with better technical framing and stronger review quality.
Startup, retention, and conversion
Reduce avoidable user loss during startup, login, loading, and first-session transitions.
Monetization and acquisition protection
Protect first-session revenue and traffic quality by reviewing ad-loading, monetization timing, and startup interaction patterns.
Integration and runtime risk control
Reduce expensive failures around SDKs, plugins, native bridges, and runtime edge cases.
The engagement model
The commercial structure is phased to reduce risk, prove value early, and scale only after evidence.
Phase 1. Diagnostic
A short, high-value review that identifies the best transformation wedge: one workflow, one product surface, one business problem.
Phase 2. Pilot
One team, one live repo, one workflow. The goal is measurable value, not abstract enablement.
Phase 3. Transformation
Multiple workflows, governance, adoption, team enablement, and leadership visibility.
Phase 4. Retainer / expansion
Ongoing optimization, rollout support, new workflow onboarding, and executive review cadence.
Why the author
Why this approach is different
This system was not designed as a white-label AI consultancy wrapper. It comes from real Unity mobile production work, technical leadership, and current architect-level platform building.
The method has been shaped across top mobile Unity environments, large-scale delivery constraints, and live production realities where startup quality, release confidence, monetization, and team leverage matter.
What a first paid engagement should produce
- A clear transformation wedge with business and engineering rationale
- A mapped workflow with bottlenecks, review surfaces, and failure points
- A pilot-ready scope
- Governance and accountability boundaries
- A shortlist of metrics tied to delivery and business outcomes
- A recommendation on whether to stop, pilot, or scale
Commercial structure
The program is designed to create a clean path from proof to transformation.
Diagnostic
2 to 4 weeks. A tightly scoped leadership and workflow review.
$20k to $35kPilot
6 to 10 weeks. One team, one live workflow, one measurable result.
$40k to $75kTransformation
6 to 9 months. Multiple workflows, governance, adoption, and operating cadence.
$100k to $250k+Advisory / Expansion
Monthly support for rollout, optimization, and new workflow onboarding.
Scope-based retainerIf your team already feels the cost of startup friction, release risk, or slow feature delivery, start with the diagnostic.
The diagnostic is the fastest way to identify one Unity workflow worth transforming before you commit to a broader rollout.