01
Marketing AI Engineering
Agents, copilots and tools
Builds AI agents, copilots and internal tools in no-code, low-code and pro-code.
Closes: AI ideas that never get built
D2C marketing engineering · AI Marketing Engineer 180
We built a different kind of engineering team for consumer marketing.
A managed marketing engineering team for D2C. Five engineering disciplines, one team, shipping from week 3.
Marketing Engineering as a Service
MEaaS ≠ SaaS.
You don't get another AI platform. You get the engineering team that makes your stack work.
- No new platform to adopt.
- No AI tool looking for a use case.
- A senior-led engineering team that works inside your stack and ships.
Week 3
First build in production
3+ specialists
Senior-led, from day one
9× faster
Week 3 vs ~7 months in-house
5 disciplines
One team, no gaps
Click to Play Video
The gap
Why we are here
D2C marketers live inside a stack of platforms, customer data, channels and AI tools. The capabilities that win can't be bought as another SaaS subscription. Someone has to engineer them.
What is a marketing engineer?
A marketing engineer builds the systems marketing runs on: AI agents, automations, API and MCP integrations, AI-ready data, search and personalization. Where marketing ops maintains tools, a marketing engineer turns strategy into working software. An AI marketing engineer puts AI at the center of that work.
The gap, in practice
- AI pilots that stall before they reach production
- A stack of platforms that were bought but never connected
- Customer and catalog data too messy for AI to use
- Products shoppers can't find, and offers that aren't personal
The market sees it too: companies now post “AI Marketing Engineer” roles at $113K–$185K base, asking one person to close the whole gap.
The challenge
One gap. Five engineering disciplines.
One title. Five disciplines. Most teams cover two. A managed MEaaS team covers all five.
02
Automation Engineering
APIs and MCP
Connects the stack: API integrations, MCP servers that let AI use your tools, routing, enrichment, monitoring.
Closes: Tools that don't talk to each other
03
Data, Skills & Context Engineering
AI-ready data
Makes data AI-ready, stands up the database or warehouse, and builds the context and skill libraries agents run on.
Closes: AI running on messy, context-free data
04
Search & Discovery Engineering
Modern information retrieval
Semantic and vector search, ranking and structured product data, so shoppers find you on Google, in AI answers and on your own site.
Closes: Products shoppers can't find, on search, in AI answers or on your site
05
Personalization Engineering
Modern recommender systems
Recommender systems that pick the next best product, offer or message for each shopper, across web, email, SMS and app.
Closes: Every shopper sees the same page, email and offer
Five disciplines, one managed team.
One person is strong in one or two of these. A MEaaS team covers the whole gap, with no single point of failure.
The MEaaS Spine
Five disciplines. One engineering spine.
The hard part isn't building an agent. It's connecting data, tools, workflows and feedback so every build compounds toward revenue.
Build lane
What gets made
Marketing AI Engineering
Agents, copilots and tools in no-, low- and pro-code
Automation Engineering
APIs, MCP, routing and enrichment
Data, Skills & Context
AI-ready data, context libraries, dashboards
- Shared contextOne source of truth on customers, brand, products and data
- ConnectionsAPIs and MCP link every tool, agent and channel
- OrchestrationAgents and workflows run the work automatically
- Engineer checkpointSenior-ledQA every build, retire what underperforms, ship the next one
- Feedback loopEvery play reports back on results
- CustomersOrders and repeat purchases
- RevenueGrowth that compounds
Growth lane
What reaches the market
Search & Discovery
Found on Google, in AI answers and in your own site search
Personalization
Recommender systems: the next best product for every shopper
Without a spineFive disciplines build in five places. Nothing compounds.
With the MEaaS SpineShared context, one set of connections, one feedback loop. Every build moves revenue.
In-house hire vs. managed team
Why not just hire one marketing engineer?
The job outgrew the job title. One person covers two disciplines. A managed team covers all five from week 1.
~9×faster to first production build than building in-house
In-house hire
High fragility- Months to find and onboard
- Strong in 1–2 disciplines, thin in the rest
- Single point of failure for every system
- Ongoing management and training overhead
MEaaS managed team
Recommended- Workshop in week 1, first build in week 3
- Specialists across all 5 disciplines
- Documented systems you own
- Scale up or down as priorities change
| Evaluation dimension | In-house solo hire | MEaaS managed team |
|---|---|---|
| Time to first value | ~7 months (3–6 to recruit, 2–3 to ramp) | Week 3, then a new build every week |
| Discipline coverage | 1–2 of 5, deeply | 2–5 of 5, by plan |
| If someone leaves | Systems stall | Team-backed and documented |
| Tooling & infrastructure | Invented from scratch | Proven templates and agent modules |
| Recruiting fees | Typically 20–25% of first-year salary | None |
| Changing scope | Re-hire or restructure | Change plan, month-to-month after 6 |
Timelines are typical industry ranges for senior technical marketing roles.
Ready to close the gap? Book a Ride-Along Workshop.
Two weeks of observing, then your first build ships in week 3. Or email info@gail180.com.
Partnerships
Pick your managed team.
Pick your team size. Every plan is senior-led. Each one starts with the Ride-Along Workshop.
| Monthly partnership | Unicorn Team 5 specialists All 5 disciplines | Core Team 3 specialists Disciplines 01–04 | Agents Team 2 specialists Disciplines 01–02 |
|---|---|---|---|
| Your team | Founder lead Marketing AI Engineer Automation Engineer Data & Context Engineer Search & Discovery Engineer Personalization Engineer | Founder lead AI & Automation Engineer Data & Context Engineer Search & Discovery Engineer | Founder lead Marketing AI Engineer Automation Engineer |
| Marketing AI Engineering | |||
| Custom AI agents & copilots (research, briefs, drafts) | Included | Included | Included |
| No-code / low-code workflow builds | Included | Included | Included |
| Pro-code apps & internal tools | Included | Included | Included |
| Interactive marketing tools (calculators, microsites) | Included | Included | Not included |
| Automation Engineering | |||
| Martech API integrations | Included | Included | Included |
| MCP servers connecting your tools to AI | Included | Included | Included |
| Customer, order & catalog data flows | Included | Included | Included |
| Monitoring, alerts & runbooks | Included | Included | Included |
| Data, Skills & Context Engineering | |||
| Context & skill libraries (brand, customer, product, prompts) | Included | Included | Included |
| AI-ready data: CRM cleanup, schema, dedupe | Included | Included | Not included |
| Performance dashboards | Included | Included | Not included |
| Warehouse / database build & pipelines | Included | Available as an add-on | Not included |
| Search & Discovery Engineering | |||
| Semantic & vector search for your site and catalog | Included | Included | Not included |
| Ranking & relevance tuning | Included | Included | Not included |
| Technical SEO & AEO (get retrieved and cited) | Included | Included | Not included |
| Structured product & content data | Included | Included | Not included |
| Personalization Engineering | |||
| Recommender models for next-best product & offer | Included | Available as an add-on | Not included |
| Real-time shopper & behavior signals | Included | Available as an add-on | Not included |
| Personalized web, email, SMS & app experiences | Included | Available as an add-on | Not included |
| Holdout testing & guardrails | Included | Available as an add-on | Not included |
| Included in every plan | |||
| Ride-Along Workshop & Build Board | Included | Included | Included |
| Weekly production ship + demo | Included | Included | Included |
| Monthly scorecard against your baseline | Included | Included | Included |
| Team training & handoff documentation | Included | Included | Included |
6 months to start, then month-to-month. available as an add-on
How we work · Roadmap to ROI
Observe. Ship. Compound.
We don't guess what to build. We watch, then ship. You get a cadence, a ship date and proof against your own baseline.
- 01Weeks 1–2
Observe
Ride-Along Workshop
We sit in on stand-ups, performance reviews and launches, then agree a ranked Build Board.
Build Board agreed
- 02Week 3
Ship
First build live
The top Build Board item goes live in your stack. In production, not in a deck.
Measured from day one
- 03Every week
Compound
Ship. Measure. Compound.
New builds. Real users. Measured results. Fast corrections.
Monthly scorecard vs. your baseline
Example Build Board items
- Catalog enrichment agent
- Shared brand & product context
- Commerce stack connected to AI
- Semantic site search
- Pages AI engines cite
- Next-best product per shopper
Yours are chosen in the workshop.
Why six months
Shipping starts in week 3. Compounding takes six months.
01
SEO and AEO compound slowly
Search rankings and AI citations take three to six months to build.
02
Recommenders need data
Personalization learns from traffic. Six months gives it enough to beat your baseline.
03
Retention takes buying cycles
Repeat purchase and LTV only show after several cycles.
04
Agents improve on your data
Version one ships in week 3. By month 5 it knows your edge cases.
05
Handoff is built in
Month 6: scorecard, training and docs. Then continue, downscope or hand off.
06
Still faster than in-house
A hire is still recruiting at month 3. We ship in week 3.
Who we are
When an Amazon marketer meets an AI PhD.
One understands how consumer marketing actually works.
The other knows how to engineer what it needs next.
Part of GAIL180, a PhD-led AI engineering team.
Meet the teamFAQ
AI marketing engineering, answered.
What is a marketing engineer?
A marketing engineer builds the systems marketing runs on: AI agents, automations, API and MCP integrations, AI-ready data, search and personalization systems. The role sits between marketing and engineering and turns marketing strategy into working software. An AI marketing engineer does this with AI at the center.
What is the difference between marketing engineering and growth engineering?
Growth engineering usually sits in the product team and optimizes sign-up, activation and retention inside the product. Marketing engineering sits in marketing and builds the systems before and around the product: data, agents, automations, SEO and AEO, content and ad pipelines that turn demand into pipeline.
What is the difference between a marketing engineer and marketing ops?
Marketing ops configures and maintains the tools a team already has: campaigns, lists, routing rules and reports. A marketing engineer builds new systems with code and AI, connects tools through APIs and MCP, and makes the data AI-ready. Ops keeps the machine running; engineering builds the machine.
What is Marketing Engineering as a Service (MEaaS)?
MEaaS is a managed team of senior marketing engineers, not software. The team works in your own tools and covers five disciplines: Marketing AI Engineering, Automation Engineering, Data, Skills & Context Engineering, Search & Discovery Engineering, and Personalization Engineering.
Is MEaaS a software platform?
No. MEaaS is a managed marketing engineering team. Senior specialists build agents, automations, data, SEO/AEO and ad systems in your own tools, and you own everything they build.
Why not hire one marketing engineer in-house?
The gap between building and growing spans five engineering disciplines. One person is usually strong in one or two, takes three to six months to recruit and two to three months to ramp, and becomes a single point of failure. A MEaaS team covers the whole gap and ships by week 3.
How fast do you start delivering?
Weeks 1 and 2 are a Ride-Along Workshop where we observe how your team works and agree a ranked Build Board. The first build ships to production in week 3, and a new build ships every week after that, so corrections happen in days, not quarters.
Why is the minimum engagement six months?
SEO and AEO take three to six months to compound, recommenders need traffic to learn, repeat purchase and LTV take several buying cycles, and agents improve on your real data. Month 6 is handoff, so you own everything. After six months it is month-to-month.
What is AEO (answer engine optimization)?
AEO is making your content the source AI assistants such as ChatGPT, Perplexity, Claude and Google AI Overviews quote and cite. It uses structured data, clear question-and-answer content, llms.txt and citation tracking alongside classic SEO.
Who owns what you build?
You do. Every agent, automation, dashboard and prompt library is built in your accounts, documented, and handed over with training.
Who is behind MEaaS?
MEaaS (also known as AI Marketing Engineer 180, Marketing Engineer 180 and AI-First Marketing) is part of GAIL180 (Generative AI Labs 180), a PhD-led AI engineering team that engineers AI solutions for every team in a business. GAIL180 was founded by Sumi Singh, PhD. AI Marketing Engineer 180 is that engineering applied to marketing.
Recommended first move
Book a Ride-Along Workshop. Ship in week 3.
Message the founders on LinkedIn with a line about your team and stack. We'll reply with how the first two weeks would run for you.
Prefer email? info@gail180.com