Slip Robotics didn’t invent a robot arm. They invented a floor that moves.

The company’s automated loading robots can load and unload trailers in five minutes, where a forklift takes upwards of an hour to do the same task. They require no dock modifications, special trailers, or IT integration. John Deere, GE Appliances, Nissan, and many other Fortune 500 enterprises run Slip in 24/7 operations.

They are the kind of product that becomes obvious once someone sees it work.

The harder part is getting the right people to see it, understand it, and believe it can work across their own operations.

Slip is creating its category. Every enterprise buyer has to move from “Wait, the floor moves?” to “We need this at every dock we run.” That journey requires education, targeted outreach, internal consensus, and credible proof across a buying committee.

Slip already had the ingredients: market knowledge, customer results, industry relationships, and a product with a measurable economic case. But much of that value still required the revenue team interpreting signals and moving each opportunity forward manually.

Slip did not need AI to invent its category story. It needed a system that could carry that story through the market without requiring its team to move every buyer forward themselves.

That was the engagement Myosin FDM stepped into.

The Brief: Build One System, Not Six Tools

The goal was not to hand Slip a collection of disconnected automations.

It was to build one AI-enabled GTM system in which market signals, account intelligence, relationships, content, customer proof, and CRM activity could reinforce one another—and leave Slip’s team fully able to operate it without us.

Slip’s marketing team owns content and workflow decisions on Slip’s side. The internal AI team owns the technical infrastructure. Together with the wider team, they brought the market knowledge and customer context the system would need.

Myosin embedded two builders on the account: Salo, our Head of FDM, and Shayna Stewart, our leading GTM Architect. They worked across parallel workstreams, but from one shared understanding of Slip’s ideal customer, buying committee, and path to adoption.

The central question was straightforward:

How do you give a lean team more capacity to create a category without separating execution from the people whose judgment makes it credible?

Beside the CRM

"Most companies look at their CRM and see the pool of customers they're already talking to," says Salo. "Slip reverses it: it looks at the ocean and asks who it can pull into the pool next."

That works because what the system learns lands in a layer Slip owns, beside its CRM. Today it holds every account the engine has researched, with its sites, buying committee, safety record and dock-automation signals. As campaigns run and the team uploads its networks, it adds every reply and every warm path. The CRM keeps doing its job. The layer beside it knows what is true about the rest of the market.

The next step, now being designed with Slip's team, turns that record into states: each account's stage worked out from evidence instead of picked by a rep, with the next action landing in the CRM.

Measure Before Building

Before a workflow went live, we established a baseline.

Eight measures were recorded with a date and source: show-lead status, cost per meeting, speed-to-lead, founder LinkedIn reach, existing proof assets, configurator conversion, AE preparation time, and ICP coverage.

The rule was simple: nothing counts as improved unless we know what “before” looked like.

That rigor fits Slip’s own product story. The case for Slip rests on measurable changes in dock time and operational throughput. The GTM system behind it should be held to the same standard.

This also changed how we approached the engagement. Success would not be measured by the number of automations launched. It would be measured by whether Slip could identify stronger opportunities, act on them faster, extend the reach of its expertise, and convert more of its customer success into usable proof.

Turning Market Activity Into Revenue Signals

Category creators encounter signals everywhere.

A prospect visits a configurator. A target account announces a new facility. Someone meets the team at a conference. A former colleague joins a company with the right operational profile. A customer produces a result that could change how another buyer evaluates the category.

The problem is rarely the complete absence of signal. The problem is recognizing which signals matter and turning them into action while they are still timely.

The Intent Engine became the center of Slip’s system. It monitors the market for buying signals, evaluates accounts against Slip’s ideal customer profile, and separates account fit from product fit. The strongest opportunities then move toward a specific next step: an AE briefing, a BDR queue item, or a nurture path.

A process that once required days of manual research can begin within minutes.

The product configurator will eventually feed this same system. Someone pricing a two-dock setup is doing more than browsing a webpage. They are revealing an operational need. Treating that behavior as a first-class buying signal allows the revenue team to respond with the right context.

Turning Leads Into Informed Follow-Up

Slip had accumulated thousands of leads across conferences annually. A conventional nurture campaign might place those contacts into a sequence and call the list activated. But a badge scan does not tell you whether someone is evaluating a solution, influencing a committee, tracking the category, or simply passing through the booth.

The conference-lead workflow suppresses accounts already involved in active deals, segments the remaining leads by engagement recency, and enriches priority accounts to reveal more of the buying committee. The result is not simply more follow-up. It is a clearer view of what follow-up should mean for each person.

The workflow also leaves behind a repeatable operating pattern. Leads from the next conference can enter a working system instead of becoming another spreadsheet waiting for someone to find time.

Turning Relationships Into Real Paths

Enterprise categories do not spread through content alone. They also move through trust.

Slip’s leaders and team members already had relationships across the market, but those connections were difficult to search collectively. Finding a warm path into a target account often depended on someone remembering that a relationship existed.

The network-mapping system turns opt-in LinkedIn exports into a queryable map of Slip’s professional network. A team member can ask who has a credible path into a target company and identify the shortest route through a real relationship.

The system never sends the message. The person who owns the relationship decides whether an introduction makes sense and reaches out personally.

This is an important boundary. AI can make a relationship visible. It cannot supply the trust that gives the relationship value.

Turning Expertise Into Market Education

Creating a category requires a company to teach the market how to see the problem differently.

Slip’s leaders already know how to do that. The constraint is the time required to turn their observations into consistent content while running the company, supporting customers, and building the category itself.

The leadership voice pipeline begins with each leader’s perspective rather than a blank prompt. It combines a documented voice, a weekly radar of dated industry signals, and a separate quality gate that rejects generic writing and unsupported claims.

AI supports the research, drafting, and review work. Slip’s leaders retain control over the ideas, claims, and final judgment.

The goal is not to generate more posts. It is to help the people closest to the market share what they know without requiring hours of drafting every week.

Turning Customer Results Into Compounding Proof

Every successful Slip deployment can make the next enterprise conversation easier.

But customer knowledge often remains scattered across call notes, performance data, interviews, approvals, and the memories of the people closest to the account. Creating each new case study becomes a separate project, and much of the underlying work gets repeated for every format.

The case-study engine creates a shared, source-tracked fact base for each customer story. From that foundation, the system drafts five coordinated assets: a written case study, video script, LinkedIn carousel, sales one-pager, and advertising concepts.

The customer approves the facts once. That approved proof can then move through the rest of the GTM system without being reinterpreted at every handoff.

This matters for more than content efficiency. In an emerging category, proof helps buyers understand what the product is, where it fits, and why adopting it is worth the organizational effort. Each customer result becomes part of the infrastructure for selling the next deployment.

The System Is Starting to Move

This case study is being written as the system comes online, not after the engagement has been wrapped in a finished success story.

The early results show the operating layer taking shape:

• Over 4,000 contacts at 129 target companies have been enriched and added to Slip's CRM, each mapped to a buying role, with account fit and product fit assessed separately.

• This year's trade-show leads were analyzed show by show, giving the team a ranked view of its leads and a playbook for the fall shows.

• The case-study engine has produced its first full customer set: one approved fact base feeding five formats, including a slide in Slip's own deck. It is now in Slip's final review.

• Leadership content is live. Two leaders approved full batches of posts with zero edits, and more of the team is onboarding as the next voices.

• The network map passed its end-to-end test, and the team's first uploads are next, under the same consent-first design it was built on.

• The value-messaging analysis is delivered: ten AI research agents produced 18 ranked angles across seven buyer personas, objection counters, five competitor battle cards and a source check on every claim. Slip can re-run it as its messaging changes.

• One data layer ties it together. Accounts, contacts, fit and signals sit in a layer Slip owns, beside its CRM, so the CRM now shows the accounts the team should talk to next, alongside the ones it already talks to. It runs in Slip's own stack and stays there."

Cost per meeting, speed-to-lead, AE preparation time, and ICP coverage are being measured against the original baseline. Full before-and-after results will follow once the measurement window closes.

Building Capacity Without Removing Judgment

Slip Robotics is asking an industry that has moved trailers the same way for decades to recognize a different possibility.

That requires more than awareness. Buyers need to understand the product, see how it fits their operation, trust the evidence, and build confidence across several stakeholders. Slip’s leaders, customer results, and industry relationships make that possible.

The AI layer gives those assets a way to work together.

It helps the team detect meaningful demand, prepare for opportunities, activate existing relationships, educate the market, and turn customer outcomes into proof. It handles work that would otherwise consume hours of research, coordination, drafting, and repackaging.

The consequential decisions remain with Slip.

That is what Forward Deployed Marketing is designed to build: not automation for its own sake, but an operating capability the company can continue to use after the original builders leave.

Slip is still creating the category. The difference is that its market knowledge, relationships, and customer proof no longer have to move through the company one manual handoff at a time.

Myosin FDM embeds senior operators inside companies to build AI-powered marketing and sales systems. Engagements run for 90 days. You own everything at the end.

fdm.myosin.xyz | olly@myosin.xyz