There is no list price for an AI marketing system, and anyone who quotes you one before seeing your funnel is guessing. What a build actually costs — in money and in weeks — is driven by three things: how clean your data is, how many tools it has to talk to, and how many workflows you want running live. So instead of inventing a number, this piece breaks cost and timeline down by scope tier, names exactly what makes a build faster or slower, and walks through the three ways I engage. Every real figure comes from a scoped proposal before any commitment — not from a page like this one.
I spent 13 years in performance marketing, including running two agencies as CEO, before going independent to build these systems hands-on. I work fully remote across the GCC and the US. So this is the operator's answer, not the brochure's.
Short version: cost and timeline scale with scope, not with a price list. A fixed-scope AI-Search Readiness Audit runs 5–7 business days. A full system build runs from a few weeks to a few months, depending on data readiness, integrations, and the number of live workflows. Fractional leadership is an ongoing monthly engagement. The cleaner your data and the fewer the integrations, the faster and cheaper it goes — and you get a scoped proposal, with the real number, before you commit to anything.
What actually drives the cost (and the timeline)
Two systems that look identical on a slide can differ by an order of magnitude in price, for reasons that have nothing to do with the AI. Three drivers do most of the work:
- Data readiness. This is the single biggest variable. If your conversion tracking is clean, your CRM is structured, and your order and revenue data are accessible, a build moves fast. If the data is scattered across spreadsheets, half-tagged ad accounts, and a CRM nobody has tended in two years, most of the cost is the cleanup *before* anything intelligent can run on top of it. AI built on dirty data just automates your wrong decisions faster.
- Integrations. Every external system the build has to read from or write to — ad platforms, CRM, e-commerce backend, WhatsApp, a payment or COD provider, analytics — adds wiring, testing, and failure modes. One or two clean integrations is quick. Six brittle ones across tools that fight each other is where weeks go.
- Number of live workflows. A single automation (say, lead scoring that writes back to your CRM) is a small, bounded job. A connected set of workflows that brief each other, hand off, and report to one place is a system. Cost and timeline track the count and the coupling, not the buzzwords.
A fourth driver is quieter but real: how much judgment has to be encoded. Mechanical work — bid adjustments, audience refreshes, report pulls — is cheap to automate. The parts that carry business judgment, like what counts as a qualified lead or when a "winning" campaign is winning on a vanity metric, take more care to get right, because that is where a careless build quietly loses you money.
Scope tiers: what "small," "medium," and "large" actually mean
I price and plan by tier because it forces an honest conversation about scope before anyone talks money.
- Tier 1 — a single workflow. One well-defined job done properly: lead enrichment, a content or campaign playbook running on rails, AI-search visibility for a set of pages. Few integrations, fast to ship, easy to measure. This is the right first step when you want proof before scale.
- Tier 2 — a connected system. Several workflows that talk to each other and report into one place — for example, paid-media reconciliation feeding lead scoring feeding follow-up. This is where most of the value lives, and where data readiness and integrations decide whether it's a few weeks or a few months.
- Tier 3 — a full operating layer. The marketing engine runs as a system, with your team operating it day to day. The build cost here is rarely the expensive part; the expensive part is the enablement, which is why I treat team enablement as its own line, not a freebie bolted on at the end.
The mistake I see most often is buying a Tier 3 ambition on a Tier 1 data foundation. The scoped proposal exists to catch exactly that before you spend.
The three ways I engage
There are three formats, and they map to the three questions you might be asking: *what's wrong, what should we build,* and *who runs it.*
- AI-Search Readiness Audit — fixed scope, 5–7 business days. A bounded diagnostic of how visible and how ready your funnel is for AI-driven search and the systems that feed it. It has a fixed scope and a fixed timeline, no public price, and you request availability rather than buy it off a shelf — because I take a limited number at a time and the calendar is the real constraint. It's the cheapest, fastest way to know whether a build is even worth doing yet.
- A System Build. This is the actual construction — Tier 1 to Tier 3 above. Timeline runs from a few weeks for a single workflow to a few months for a connected system, set entirely by the three drivers. Every build starts from a scoped proposal with a real number and a real timeline; I don't quote builds blind. See what I've built for the shape of the work.
- Fractional leadership. An ongoing monthly engagement where I own the marketing-systems strategy and direction without you hiring a full-time senior leader. This is for companies that have, or are building, a system and need someone accountable for it — not a one-off project, but a standing relationship.
Most engagements start with the audit, because it's the lowest-risk way to replace guesses with a scoped plan.
What makes a build faster and cheaper
If you want to compress both cost and timeline before we even talk, this is the list:
- Clean, accessible data. Conversion tracking that fires correctly, a CRM with real structure, revenue and order data you can actually export. This alone can halve a timeline.
- Few, stable integrations. Fewer tools, well-chosen, beats more tools loosely stitched.
- A narrow first scope. One workflow shipped and measured beats five half-built. You can always add — and a working Tier 1 funds the argument for Tier 2.
- A decision-maker in the room. Builds stall on unanswered questions, not on engineering. Someone who can say yes keeps the clock running.
What makes it slower and more expensive
- Dirty or locked-away data — the most common, most expensive cause, because the cleanup happens before any of the interesting work.
- Integration sprawl — every extra system the build must touch multiplies the testing surface.
- Scope that grows mid-build — the classic. New workflows added halfway in reset the timeline. Better to ship the first scope, then re-scope deliberately.
- No clear owner of the outcome — if nobody is accountable for what the system is *for*, you get an expensive thing that runs and produces nothing bankable.
Is it worth it? The honest framing
The point of spending anything is the return on the other side. Here's the credibility, reported with my two-number rule — every result shown as the platform number *and* the real collected number, with the gap named. These are outcomes the systems produced, not build prices — but they're the reason the build question is worth asking at all.
- Education (FIT Institute, named with consent). Ad spend of 121,330 AED produced roughly 912,550 AED in collected revenue — about 7.5x clean ROAS. Because education has no physical product to return, the gross and collected numbers converge here, so 7.5x is the real figure, not a flattering gross-before-returns.
- An anonymized Egyptian COD store. 137K EGP in spend produced 564K EGP — 4.1x gross, but 1.9x delivered once roughly 33% of orders came back as returns. A build that managed only to the dashboard would have called this a win.
- My own AI-SEO SaaS. 1,230 leads at about $6.50 each on roughly $11.9K of spend — the kind of unit economics a well-built acquisition system is supposed to hold.
None of those are build prices — they're what the build is *for.* The cost question only makes sense next to the return, which is why I won't separate them.
FAQ
How much does an AI marketing system cost?
There is no single price, because cost is set by scope — data readiness, integrations, and the number of live workflows — not by a menu. A single workflow is a small, bounded job; a full operating layer is a different order of magnitude. The honest answer is a scoped proposal against your specifics, before any commitment.
How long does it take to build?
The AI-Search Readiness Audit is fixed at 5–7 business days. A system build runs from a few weeks for one workflow to a few months for a connected system. Clean data shortens it more than anything else.
Why won't you publish a price?
Because a published price is either so wide it's useless or so specific it's wrong for your funnel. The audit is fixed-scope and you request availability; builds and fractional work are quoted from a scoped proposal. I'd rather give you a real number than a comfortable one.
What's the cheapest way to start?
The audit. It's the lowest-cost, fastest way to find out whether a build is worth doing yet — and it replaces guesses with a plan you can actually price.
Next step
If you're weighing a build, start by knowing what's actually there. The audit tells you whether your data and funnel can support the system you have in mind — before you spend on building it.
Want this run on your own funnel? Request a systems diagnostic — I'll show you what's working, what's leaking, and what's worth building, with the gross and the delivered number. Prefer a quick message? WhatsApp me.