Questions we
actually get.

It's the difference between individuals using AI tools and a team that has made AI part of how it works. AI-native means every role, product, design, engineering and QA, builds with AI as a core part of how they deliver, against shared standards the whole team trusts. One practice, governed and traceable, not a private habit on the side.

On our five-level maturity scale it's the top end: the routine is AI-assisted or AI-led, and people spend their time on architecture, design and judgment instead of typing and chasing bugs. Getting there is what the engagement is for.

Almost every team we talk to has tools in place. The problem is usually that usage is uneven. Engineers adopted Claude Code or Cursor, but PMs still write the same PRDs, designers hand off the same way, and nobody has a shared standard for what good looks like. The gains stay individual and slip.

Aitonomy does two things at once: it measures where each project and codebase actually stands on AI readiness, scored against the five-level maturity scale, and it installs a way of working across every role so the team can move up and stay there. The platform gives you the picture. The engagement changes the practice. Both happen together.

Any digital product team with an active product team and a mandate to move. Company size doesn't determine fit. What matters is that someone in leadership is accountable for AI output and wants a model to deliver it. That person is typically a CTO, a digital director, or an engineering lead.

This is the point. Coding tools capture roughly 16% of the software development lifecycle. The other 84% is planning, design, review, governance, and handoffs between roles. We train every role: PM, design, QA and leads, not just engineering.

If only engineers change how they work, the org captures about 30% of what's available. Every role has to move for the gains to hold.

The platform connects to your stack and reads your team's AI readiness in a day. An engineer interviews your PMs, designers, engineers, and leads, maps workflows across every role, and measures readiness per project and per codebase. You leave with the full picture before any commitment.

You get two things. A scored report: every project mapped against the five-level maturity scale, where the gaps are, where the quick wins sit, and what it takes to move each one up. And a scoped path forward: a fixed-price, milestone-gated proposal for the engagement ahead, scoped to your team's current level. The report is yours to act on however you like. No commitment needed to get it.

A senior forward-deployed engineer works inside your team. Not observing, not briefing, but building alongside your PMs, designers, and engineers. The practice transfers in the work, not in a slide deck.

The engagement runs in three phases: starting with a one-day AI readiness check to see where the team stands, then working inside the team role by role until the new way of working holds, then stepping back while the team runs independently. Each phase has an evidence-based exit gate, not a calendar deadline. We don't move on until the gate passes.

The engineer is in your standups, in your PRs, working with your team on real tickets. Not running workshops from the outside, but doing the work alongside the team so the new ways of working are demonstrated, not described.

The team sees it working in their own codebase on their own backlog. That's how adoption takes hold. Training on abstract examples doesn't change the way people work. Working differently on real work does.

The Readiness Scan takes a day. The platform connects to your stack and repos, an engineer interviews PM, engineering, design, and leads, and you have a full readiness picture by end of day. No prep needed, delivery keeps running.

The full engagement is more intensive at the start, when the engineer is working across every role to get the new way of working embedded. It gets lighter as the team finds its rhythm. The tradeoff: the practice takes hold faster when the team works with it daily on real work, not in separate sessions.

Each phase ends when the evidence says it should, not when the calendar runs out. Exit gates are a checklist of observable things: the team runs a process unassisted, the platform confirms a maturity level, the compliance posture passes a review.

This matters because it means we don't move on when the team isn't ready, and we don't drag out phases that have already passed. It's the mechanism that keeps the delivery honest.

It runs from L0 (no AI in the workflow) through to L5 (selective end-to-end autonomy on bounded, well-understood work). Most teams we meet are at L1 or L2: individual tool adoption, no shared methodology.

L0: No AI in workflow.
L1: AI for code review and docs. Human in control.
L2: AI as co-pilot. Autocomplete, refactoring, test generation.
L3: AI as co-author. AI drafts, humans review.
L4: Spec-driven, AI-led. PMs write specs, AI implements.
L5: Selective autonomy. End-to-end on bounded workflows, audit guardrails.

The Readiness Scan scores your team across all five dimensions (practice, trust, governance, platform, autonomy) per project and per codebase. That's your baseline.

The platform sits between your team and the AI. It covers four things: Context (your constitution, ADRs, skills, and contracts read by every AI call), Performance (right model for the task, safeguards on every prompt, automatic failover), Compliance (every AI call traceable from prompt to PR, AI Act aligned by construction), and Insights (maturity, adoption, cost, and ROI measured per team and project over time).

It's what makes the practice stick after the engagement ends. The platform keeps the team honest as the tooling, regulation, and team composition evolve.

Yes. The platform is model-agnostic and tool-agnostic. It works with Claude Code, Codex, Cursor, VS Code, OpenCode, and more on the developer side, and routes to Anthropic, OpenAI, Google, Mistral, Aleph Alpha, or self-hosted models on the inference side.

Developers keep the tools they already use. The platform sits in between, adding context, governance, and observability without replacing anything.

The router sends each task to the right model for the job: cheap models for documentation and summaries, premium models for complex refactors, local models for sensitive contexts. Prompt caching handles shared, repeated context. The result is typically 30-60% lower LLM spend, usually recovered in the first quarter.

Most teams we assess have no per-PR or per-project cost visibility. The first thing the platform gives you is that picture, and it's usually a surprise.

Fully EU-native. The platform runs on EU-hosted infrastructure by default and is deployed in your environment, so your data stays in your instance and within the EU. Customer-managed encryption keys are available for teams that need them.

The EU-sovereign models we route to (Mistral, Aleph Alpha, self-hosted) run entirely in-region. Where a team chooses a US model, inference is EU-routed so requests stay within EU jurisdiction. EU-sovereign is the default, not a configuration option.

About 30 minutes. The developers who want to take part sign up, and you register the GitHub App for your repos. That's the whole setup.

The engagement is milestone-gated, not time-based. Each phase ends when its exit gate passes on evidence, not when a calendar runs out. If a phase takes longer to pass its gate, we work through it.

It changes the incentive. We're set up to deliver the gate and step back, not to stay. That's the accountability model we want.

Two things, regardless of what comes next. First, a Readiness Report: every project and codebase scored against the five-level scale, with the gaps and the quick wins mapped out. Second, a fully scoped proposal for the engagement ahead: defined phases, exit gates named.

The report is yours to act on however you like. Some teams use it internally to prioritise where to focus. Some take it to the board. You don't owe us anything beyond the Readiness Scan fee.

The team keeps everything it has built: the constitution, ADRs, skills and contracts stay in place, and the new way of working holds with or without us. The gains so far are yours to keep.

Keeping the platform is then a choice. Teams that want ongoing visibility, compliance evidence and cost management keep it on a subscription; teams that don't can stop and lose none of what they've gained. No standing retainer, no lock-in. If standards or tooling shift enough to matter, a returning engineer can scope a focused engagement later.

Hiring solves a capacity problem. Aitonomy solves a capability and methodology problem. The skill that's scarce now isn't writing code. It's knowing how to build and govern AI-native workflows across an entire team. That capability doesn't exist in the talent market at scale, and a new hire who lands in an unchanged team quickly reverts to the existing way of working.

We install the practice across the team you already have, in a defined engagement, and leave. The gains persist without headcount.

The EU AI Act requires teams to classify AI usage by risk, maintain audit trails, and demonstrate accountability for AI-assisted decisions. That's not a bolt-on. It has to be built into how the work runs.

The platform's compliance module traces every AI call from prompt to merged PR, runs AI Act risk classification on every PR, and generates an evidence portal for auditors on demand. Quarterly compliance reports take one click, not a week. Sector-specific requirements (finance, health, government) are supported. Compliance is by construction, not by configuration.

Especially relevant. The hardest part of AI adoption in regulated industries isn't building the workflow. It's making compliance, legal, and leadership trust it. The platform addresses the governance problem structurally: full audit trail, explainable decisions, sector-specific classification, EU-sovereign data handling.

Finance, health, and government sectors have additional context built in. If your risk profile is specific, the Readiness Scan will map it precisely.

The performance module handles this automatically: PII is redacted before prompts leave your network, secret detection catches API keys, tokens, and credentials, and compliance-aware routing keeps sensitive data off third-party providers. Customer-controlled retention policies apply. Zero accidental leakage by default.

Two things. First, the engagement doesn't end until the team can demonstrate it runs independently. That's the exit gate for the Validate phase. We don't leave until the evidence says the practice is holding.

Second, the platform stays live. The constitution, ADRs, skills, and governance layer continue running after we leave. Drift gets caught because the platform is still reading every AI call and PR against the standards the team set. Nothing is left to memory or goodwill.

The platform carries the institutional knowledge. A new engineer joins and immediately works inside a codebase where the constitution, ADRs, and skills library are live and enforced. Onboarding accelerates because the standards aren't in someone's head. They're in the platform.

For teams that want structured onboarding, a returning engineer can run a focused engagement to get new joiners up to the team's level. That's scoped separately when the need arises.

Yes, and that's expected. AI-native standards and compliance requirements keep moving. The platform receives updates continuously. When there's a meaningful enough shift, a new model capability worth adopting or a regulatory development that changes the compliance posture, a returning engineer scopes a focused engagement to help the team absorb it.

No standing retainer. No dependency. We come back when it's warranted.

Yes. The product team is usually the right place to start. The methodology takes root fastest there, and results are visible fastest there. Once the product team is the internal proof point, the same framework and the same approach applies outward to adjacent departments: data, operations, marketing.

That expansion is a separate scoped engagement. The product team's experience and the platform already in place make it significantly faster the second time.