Developers and ML leads scroll past ad copy that sounds like every other AI startup. We build paid social that signals you know what you are talking about, then we connect those impressions to real pipeline. No vanity reach, no last-click theater.
The Problem
Your buyers are trained to ignore ads
The people who decide whether to adopt your model or platform are engineers, ML researchers, and technical founders. They run ad blockers, they distrust marketing language, and they can smell a generic benefit statement in half a second. A LinkedIn ad that says 'transform your business with AI' gets zero respect from someone who can read your docs and judge your architecture. The bar for what counts as credible to this audience is far higher than for a normal SaaS buyer.
Lead-gen forms attract the wrong people
When you optimize paid social toward form fills, the platform finds you the people most likely to fill out forms. For AI companies that often means students, tire-kickers, and competitors doing research, not the VP of Engineering with budget. You end up with a CRM full of low-intent contacts and a sales team that stops trusting the channel. The cheapest lead is rarely the one that closes.
The enterprise cycle breaks your attribution
A technical buyer might see your X post in March, read a Reddit thread in May, and request a demo in September after their CTO approves a budget. Last-click attribution credits whatever they clicked last and tells you paid social is dead weight. So you cut the budget that was actually seeding the pipeline. The long, multi-touch AI sales cycle makes naive ROAS dashboards actively misleading.
PLG signups stall before they pay
Plenty of AI companies get developers to sign up for a free tier, then watch them go dark. Paid social usually stops at the signup and never works the gap between activation and paid conversion. The result is a top-of-funnel that looks healthy and a revenue line that does not move. Retargeting the right product signals is the part most teams skip.
How We Help
We start by figuring out who actually buys and who actually blocks. For most AI and ML companies the economic buyer, the technical evaluator, and the end user are three different people on three different platforms. Before we touch ad accounts we map that, audit what you have already spent, and pull the conversion paths apart so we can see which touches preceded real revenue, not just which one got the last click.
Then we decide what paid social is even for in your case. Some AI companies should be building an audience of developers and ML practitioners who will trust you when they eventually have budget. Others have urgent pipeline pressure and need lead-gen now. These two goals pull in opposite directions, and pretending you can do both at full intensity is how budgets get wasted. We make the tradeoff explicit and weight spend to your stage and runway.
Execution is platform by platform. LinkedIn reaches the VP of Engineering and Head of ML through job-title and skills targeting, and account-based lists work there for enterprise. Meta is cheaper reach for retargeting and founder-led brand. X and Reddit are where the technical community lives, and a single credible thread or sharp demo clip beats any polished ad. We run them as one system, not four disconnected campaigns, because the buyer crosses all of them.
Creative decides whether technical buyers stop scrolling. We do not ship stock-photo-and-buzzword ads. We build creative that signals technical credibility: real product surfaces, benchmark numbers stated honestly, a founder or engineer talking like an engineer, code and architecture shown instead of described. This is where we coordinate closely with your creative work so the ads match the substance of the product, and where our team partners on the systematic testing that finds what a skeptical audience responds to.
Retargeting is built around product signals, not page views. We segment PLG signups by activation behavior and push different paid social to someone who hit an API limit versus someone who never made a call. We move free-tier developers toward paid conversion with messaging that meets them where they got stuck. That is the bridge between a healthy signup chart and revenue.
Measurement is where we refuse to lie to you. We instrument multi-touch paths so the audience-building work that seeds enterprise deals gets credit, hold last-click as one view among several, and tie spend to pipeline and closed revenue on the real timeline. We would rather report an honest 90-day lag than a flattering daily ROAS that falls apart at the board meeting. Through all of it we plug in as a fractional growth operator, not an agency that sends decks: embedded with your team, owning the numbers, working the way an in-house VP of Growth would if you could afford one full time.
What we deliver
- Buyer and blocker map – who evaluates, who approves, and who uses the product, with the platform each one lives on
- Spend audit and attribution rebuild – a clear read on which past touches preceded real revenue, not just last clicks
- Audience vs lead-gen weighting – an explicit budget split tied to your stage, runway, and pipeline pressure
- LinkedIn ABM and title-targeted campaigns aimed at engineering and ML decision-makers
- X and Reddit community creative – threads, demo clips, and posts that earn respect from technical practitioners
- Credibility-first ad creative – product surfaces, honest benchmarks, and engineer-voiced messaging instead of buzzwords
- PLG retargeting by activation signal – distinct paths for stalled signups, API users, and free-tier developers near a limit
- Multi-touch measurement instrumented to your actual AI sales-cycle length, reported against pipeline and closed revenue
If your paid social can be read and approved by someone who has never written a line of code, it will be ignored by the engineers who decide whether you get adopted.
Our Methodology
We run paid social as a 90-day sprint because the AI sales cycle is too long to judge campaigns week to week and too expensive to drift for a year. Days 1 to 30 are diagnosis and setup: we audit existing spend, rebuild attribution to see real conversion paths, map buyers and blockers, and decide the audience-versus-lead-gen split. By the end of month one we have a small set of live campaigns and instrumented measurement, not a strategy deck.
Days 31 to 60 are systematic testing. We run credibility-first creative across LinkedIn, Meta, X, and Reddit, isolate what technical buyers actually respond to, and kill the variants that only attract low-intent fills. We tighten targeting around the job titles and communities that produce qualified pipeline and start working PLG retargeting against activation signals.
Days 61 to 90 are scaling what proved out and reporting against the timeline that matters. We push budget into the audiences and creative that fed real opportunities, formalize the multi-touch view so audience-building gets fair credit, and hand you a model of what each platform contributes to pipeline. By day 90 you know what to keep funding and why, in revenue terms a board will accept.
How We Work
We engage as a fractional growth operator embedded with your team, not an external agency on a retainer. In the first 30 days the priority is truth: we get into your ad accounts, CRM, and product analytics and tell you what is actually working before we change anything. You get an honest read on past spend, even when it is unflattering.
Through days 31 to 60 we shift to execution and testing. We are in your Slack, in your standups when it helps, and we treat pipeline as the scoreboard. The cadence is a weekly working session on numbers and creative plus async updates as campaigns move, not a monthly status call where you learn what happened a month late.
By days 61 to 90 we are scaling the winners and building measurement that survives a board review. We coordinate directly with whoever owns creative and product so the ads stay honest to what you ship. You always know what is running, what it costs, and what it returns against the real sales cycle.
The team is senior. You work with the operator doing the thinking, supported by specialists on creative and measurement when needed. No junior account manager learning on your budget, and no layers between you and the person accountable for the number.
- A paid social program that reaches actual technical decision-makers instead of form-fillers
- Creative that earns credibility with engineers and ML leads rather than getting scrolled past
- Attribution that credits the audience-building work seeding long enterprise deals
- PLG signups moved toward paid conversion through activation-based retargeting
- Spend reallocated from vanity reach into the platforms and audiences that produce pipeline
- A clear, board-ready model of what paid social contributes to revenue on your real timeline
If your ai / machine learning company needs paid social leadership, we should talk.
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Frequently asked questions
What does paid social for an AI company cost?
Our fractional engagement typically runs $10K-$30K per month plus your media spend, depending on how many platforms you are running and how much creative production is involved. Media spend is separate and varies widely by stage; a Series A company often starts in the $20K-$50K per month range across channels.
How long before paid social produces pipeline for an AI product?
You will see signal on creative and audience quality within the first 30 to 45 days, but real pipeline tracks your sales cycle. For a developer tool with a short PLG motion, paid conversions can show inside a quarter.
How do you integrate paid social with our existing growth stack and team?
We plug into your ad accounts, CRM, and product analytics directly so the measurement reflects real behavior, not platform-reported conversions. We work inside your tools and your Slack rather than running a separate agency process on the side.
Why hire Winston Francois instead of a paid social agency?
Most agencies optimize the metric that is easy to report, which for paid social means cheap leads, and cheap leads for AI companies are usually the wrong leads. We work as an embedded fractional operator who owns the pipeline number, not a vendor protecting a retainer.
How do you measure ROI when our sales cycle is long and multi-touch?
We build attribution around the real path to revenue, treating last-click as one view among several rather than the truth. We instrument multi-touch tracking so an X post in March and a Reddit thread in May get credit for a deal that closes in September.
Is paid social even worth it if our buyers are skeptical engineers?
It is worth it if you run it for credibility and audience first, and lead-gen second. Skeptical engineers do not convert off a single ad, but they do remember a sharp demo clip, an honest benchmark, or a founder who talks like one of them.
