Every AI vendor is hitting the same accounts with the same automated outbound this quarter. Technical buyers built an immune system against it. An SDR motion that works for AI/ML companies sounds like a peer, not a pitch, and is judged on qualified conversations – not dials.
The Problem
Technical buyers can smell automated outbound from the first line
Engineering leaders and ML practitioners read code reviews and vendor docs all day. A templated 'I noticed you're scaling fast' email gets archived in under a second. The buyers who decide whether your model or platform gets evaluated are exactly the people most allergic to being sold to, and a generic SDR motion trains them to ignore your domain entirely.
Every AI company is cold-emailing the same 500 accounts
The target list for an AI/ML vendor is small and obvious – the same logos every founder wants. Those accounts now get dozens of near-identical outbound touches a week from competitors using the same data tools and the same playbook. Standing out means a point of view a buyer hasn't already deleted twice today, and most teams have nothing to say beyond their feature list.
PLG signups pile up and nobody knows which ones to call
Product-led AI companies generate a flood of free signups, API keys, and trial accounts. Treating them all the same wastes SDR hours on hobbyists while a real buyer who just wired your model into production gets no human follow-up. Without a way to read product usage signals and time the outreach, the best-qualified hand-raises go cold.
Pipeline gets blamed for a sales cycle that was always going to be long
AI/ML purchases run through security review, model benchmarking, data-privacy sign-off, and a technical proof of concept. That cycle is measured in quarters, not weeks. When leadership grades SDR output on closed revenue this month, the team optimizes for meetings that book fast and die fast, and real evaluations that take two quarters never get the credit or the patience they need.
How We Help
We start by getting specific about who actually decides. For an AI/ML company that usually means a technical economic buyer (a VP of Engineering or Head of ML) and a hands-on champion who has to live with your tool. We map what each one cares about, what they read, and what makes them trust a vendor enough to spend evaluation time. That research is the difference between outreach a buyer answers and outreach they report as spam.
Next we build a message that sounds like it came from someone who has shipped what your buyer is shipping. No 'quick question,' no fake personalization tokens. We write outbound around a real technical insight – a benchmark, a failure mode in the buyer's current stack, a tradeoff they are actively arguing about internally. The goal of the first touch is to earn a reply, not to book a demo. Demos come after the buyer believes you understand their problem.
We treat PLG signups as a separate, higher-intent motion. We connect product usage signals to the outbound queue so an SDR reaches out when a signup crosses a threshold that means real intent – production API calls, a team inviting colleagues, hitting a usage tier. The outreach references what the user actually did in the product, which makes it land as helpful instead of intrusive.
On tooling: yes, we use AI in the outbound stack, and we are careful about it. AI does the research and drafting grunt work – pulling a buyer's recent talks, summarizing their tech blog, surfacing accounts with hiring signals. A human writes the line that gets sent. We never ship machine-generated copy to a machine-savvy buyer, because that is the fastest way to lose the room.
We align with sales on what a qualified conversation is for a long evaluation cycle, and we measure SDR work against sourced pipeline and meeting-to-eval conversion rather than raw dials. That keeps the team from chasing easy meetings that never become deals, and it gives leadership an honest read on pipeline contribution across a multi-quarter cycle.
The Winston Francois difference is that we run this as an embedded growth function, not a dials-per-day vendor. We sit between your founder, your product team, and your sales leaders, and we build the SDR motion as part of your wider go-to-market system – so messaging, qualification, and measurement all point at the same revenue number instead of fighting each other.
What we deliver
- Buyer and champion research profiles for your core technical accounts
- Outbound sequences built on a technical point of view, written for engineers
- PLG signal-to-outreach playbook connecting product usage to the SDR queue
- Qualification framework and definitions tuned to a multi-quarter evaluation cycle
- Pipeline reporting on sourced opportunities and meeting-to-eval conversion
Technical buyers do not reward volume – they reward a vendor who clearly understands the problem they are wrestling with. The AI/ML companies that win outbound send fewer, sharper touches that read like a peer, not a pitch.
Our Methodology
Our build runs as a 90-day installation of an SDR motion, not a campaign you switch on. The first 30 days are research and positioning: we interview your sales and product leads, study won and lost deals, and define the buyer, the champion, and the technical insight each outbound thread will hang on. We also wire up the PLG signals that separate a real buyer from a curious tinkerer.
Days 31 to 60 are about building and testing the sequences. We write and run a small volume of outbound, read the replies, and tune the message until technical buyers are answering instead of ignoring. We treat early outbound as research – what insight earns a reply tells us as much as any open rate, and we feed that back into the message.
Days 61 to 90 install the operating cadence. SDR work is graded on qualified conversations and sourced pipeline against the long evaluation cycle, weekly reviews keep sales and the SDR motion aligned on what qualified means, and we hand off a documented motion your team can run without us if you choose to bring it in house.
How We Work
Initial engagements run 3 to 6 months because building an SDR motion that technical buyers respect takes a quarter of message iteration plus part of an evaluation cycle to read pipeline. The first month is research and positioning. The second is sequence build and live message testing. From there we run the motion and report against sourced pipeline.
Our team includes an outbound strategist who owns the motion, a researcher who builds buyer and account intelligence, and a copy lead who writes outbound that does not sound like a robot. From your side we need access to sales leadership, product usage data for the PLG signals, and a technical voice we can interview for the insights the outreach is built on.
Weekly reviews track reply quality, qualified conversations, and meeting-to-eval conversion. Monthly reviews tie SDR activity to sourced pipeline and movement through the evaluation cycle. Most AI/ML companies see reply quality and qualified meetings improve within 60 days, with sourced pipeline becoming readable around 90 days and revenue impact landing on the buyer's own eval timeline.
- Higher reply rates from technical buyers who normally ignore outbound
- PLG signups converted into qualified sales conversations at the right moment
- Cleaner pipeline measured against the real evaluation cycle, not vanity meetings
- An SDR motion documented well enough to bring in house when you are ready
If your ai / machine learning company needs sales development (sdr/bdr) leadership, we should talk.
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Frequently asked questions
How much does an SDR/BDR program cost for an AI/ML company?
Most engagements run between $15K and $40K per month depending on outbound volume, how much research each account needs, and whether we are running the motion or building it for your in-house team to run. That is less than the fully loaded cost of hiring and ramping a senior SDR plus a manager plus the tooling stack.
How long before we see results from an SDR motion for AI/ML outbound?
Reply quality and qualified meetings usually improve within 60 days as the message gets sharper and starts landing with technical buyers. Sourced pipeline becomes readable around 90 days once enough opportunities have entered the funnel. Closed revenue follows your evaluation cycle, which for AI/ML purchases often runs two to four quarters because of security review, benchmarking, and proof-of-concept work. We grade the motion on leading indicators early and revenue once the cycle allows it.
How do you integrate with our product data and sales team?
We connect to your product usage signals so the SDR queue knows when a PLG signup crosses into real intent, and we sit in your weekly sales cadence to keep qualification aligned. We need access to your CRM, your outbound tooling, and a technical voice we can interview for the insights outreach is built on. We do not require daily engineering time beyond reviewing technical accuracy on the messaging.
What makes Winston Francois different from a typical outbound agency?
Most outbound agencies sell dials and meetings booked, which is exactly the spray pattern technical buyers have learned to ignore. We build the SDR motion as an embedded growth function tied to your wider go-to-market, with research-led messaging written by humans and measured on sourced pipeline. We use AI for the research and drafting grunt work but never ship machine-written copy to a machine-savvy buyer.
How do you measure ROI when the sales cycle is this long?
We separate leading indicators from lagging ones. Early on we report reply quality, qualified conversations, and meeting-to-eval conversion, because those move within a quarter and predict pipeline. Once opportunities mature we report sourced pipeline and influenced revenue against the named accounts and PLG cohorts we targeted. Grading SDR work on closed revenue alone in month one is how teams end up chasing meetings that book fast and die fast, so we tie measurement to where each deal actually is in the evaluation cycle.
What type of AI/ML company is the right fit for this service?
Series A to growth-stage AI/ML companies between $5M and $100M in ARR that sell to technical buyers and need outbound to complement, not replace, product-led growth. The strongest fit has a defined ICP, at least a couple of account executives who can run technical deals, and product usage data we can read for PLG signals. The first step is a free pipeline and outbound audit to find where technical buyers are dropping off and which PLG signups are going unworked.
