AI Growth System – Install
The growth machine your team owns after I leave
Led growth at cnvrg.io before its acquisition by Intel.
I lead and personally implement the system build we agree: selected paid, buyer-content, search-readiness, measurement and reporting workflows connected to your stack. We define delivery capacity, team dependencies and the continuing owner, then test and document handoff. The install and ongoing operation have separate responsibilities.
An installed system, not a retainer
The AI growth system is not a retainer and not an agency. It is a set of five engines installed into your company, wired to your stack, and handed to your team to run. You end up owning the machine, not renting the operator.
“An engagement should leave a machine behind, not a dependency. If your team is not stronger and more self-sufficient the day I hand off than the day I started, the install failed.”
Compare the actual delivery, asset ownership, access and continuity in an agency, hire or installation proposal. I personally build the selected growth workflows and document how the client owner operates and maintains them. Licences, recurring services and any specialist support remain explicit in the handoff.
At Elementor I led acquisition during company growth from $200K to $20M ARR, connecting paid acquisition, activation, lifecycle and measurement with the wider team. The systems and team continued after my handoff. That experience informs how I build, document and transfer the growth systems in this offer.
One machine, five engines, your team runs it
Paid acquisition engine
Campaign structure, creative testing loop, and budget rules across Meta and Google, wired to real profit signals instead of platform-reported ROAS. Your team keeps running it from a documented playbook, not from my memory.
GEO and AI-citation engine
I personally implement the agreed buyer-answer content, accurate entity information, applicable markup and access checks, then record comparable source-citation observations. The engine decides whether to include a page. Monitoring and implementation acceptance are distinct from an answer appearing.
Content and demand pipeline
A repeatable pipeline from buyer question to published, citable asset: briefs, structure, internal linking, and a quality gate. Not a content calendar. A machine that turns your domain expertise into pipeline your team can operate weekly.
Attribution OS
I implement reporting from the agreed customer, cost and revenue records, with defined matching rules and observation windows. Reconcile the selected sources, retain unresolved cases and review the business decision; an attribution model does not establish every journey or causal contribution.
Growth dashboards and alerts
I build the agreed spend, funnel, pacing and alert views, showing source freshness and unavailable inputs. Test the chosen thresholds and failure cases, then name the owner who investigates and recovers the workflow. Refresh and response times follow the actual source and operating scope.
Diagnose, install, train, hand over
Diagnose the real constraint
I inspect the agreed funnel and source records, validate the suspected constraint and implement the first work the evidence supports. Name missing data, access and product or team dependencies before choosing the next system.
Install the engines
Stand up the paid, GEO, content, attribution, and dashboard systems, wired to your stack and data. Each one ships with a documented playbook, so the deliverable is an operating system the team keeps running, never a slide deck.
Paid acquisition: agreed campaign, creative and budget inputs become a campaign structure and testing playbook.
GEO: buyer questions, approved facts and priority pages guide page changes and citation observations.
Content: buyer questions and source material become briefs and drafts for a named editor to approve.
Attribution and dashboards: agreed definitions and available spend and revenue records feed reviewed reports and views; name the owner who investigates gaps and anomalies.
Train the team who will run it
Work alongside your in-house people so they own each engine before handoff. The bar for the role: the team is stronger and more self-sufficient on the day I leave than the day I started.
Name a client-side owner for each engine and make its instructions, required access and data definitions easy to find.
Have the owner follow an agreed normal task from input to output and explain where human decisions are required.
Check an agreed failure example: how the problem appears, who responds and which recovery option is available.
Identify missing access or behavior still awaiting a check before ownership transfers.
Own it, with a safety net
At handoff, verify the agreed client-controlled assets, operating instructions, owners and recovery checks. Licences, recurring costs and continuing maintenance remain visible. Optional continuing review is scoped separately, including its cadence, responsibilities and commercial terms.
Operator seat, AI infra, or Tools
| Operator seat | AI infra | Tools | |
|---|---|---|---|
| What it is | I run growth end to end | I install the machine your team runs | Self-serve audits + tools |
| Best when | You want an owner, not a hire | You have a team, need the system | You want to diagnose first |
| Delivery | Done for you | Done with you | Self-serve |
| After the engagement | Ongoing or clean handoff | You own and run it | Yours to keep |
| Where to go | AI Premium | This page | Free tools |
What an install looks like end to end
Relevant operating experience: Elementor
Before: a product with strong momentum and an opportunity to connect acquisition with activation, paid conversion and lifecycle records.
What went in: my acquisition work included campaigns, creative testing, customer journeys, lifecycle and revenue measurement alongside the wider team.
After: company ARR grew from $200K to $20M during that period, and the systems and team continued after handoff. The case illustrates the acquisition, activation, lifecycle and measurement work that informs how I build and transfer systems today.
That is the shape of an install: diagnose the real constraint, build the engines, train the owners, hand over a system that survives you.
Why install instead of retain
There are two honest ways to buy senior growth. You hire an operator to run it for you, or you install the system and run it yourself. The operator seat is the right call when you want one accountable owner of the number and do not yet have a team to hand a machine to. The install is the right call when you already have capable people who are missing the system, the instrumentation, and the playbook, not the headcount.
A documented installation makes responsibilities and continuity reviewable. It still needs a trained owner, current access, maintained dependencies and a recovery process; documentation does not eliminate key-person risk or continuing costs. Compare that responsibility with the coverage supplied by an ongoing operating engagement.
Tell me what your team already runs and what is missing
Bring the customer problem, current stack, evidence and team. We can decide whether a personally implemented install, continuing operating role or focused diagnostic fits, then agree the first work, capacity, access and review window. Duration and fees follow the actual scope.
Sources: Marketing automation (Wikipedia), Attribution (marketing) (Wikipedia)
Where this fits
This is the AI infra tier. Compare it against the Operator seat (AI Premium) and the free tools and audits on the three ways to work page, or see how it runs for a specific vertical on the SaaS growth system. Full pricing is on the pricing page.
AI growth system FAQ
What is an AI growth system?
How is this different from the operator seat (AI Premium)?
Who runs the system after you leave?
How long does an install take?
Does this work for both Israeli and global companies?
What does the GEO and AI-citation engine actually do?
I personally implement the selected buyer content, access, accurate entity information and applicable markup, with a documented monitoring method. Record source citations, mentions and unavailable observations separately. The engine chooses its sources; a delivered configuration or writing format does not guarantee inclusion.
What do I actually receive at the end of an install?
You receive an operating system your team keeps running, never a slide deck. Each engine ships with a documented playbook and is wired to your stack and data. I work alongside your in-house people so they own each engine before handoff, and you own the machine afterward.
Work with Yaniv
Fractional Head of Growth for B2B SaaS, B2C, and e-commerce. Revenue that compounds, not spikes. Any channel, any motion, any stage.