Find a reason
to go after the account.
The agents read filings, earnings calls, and public sources, then shortlist accounts with pressures the product can address.
Output → Account priorities & why now
Industrial GTM. One-to-one. At scale.
Our cold emails alone have caused direct escalation from defence, aerospace & industrial orgs within 72hrs.
An industrial GTM platform that pulls intelligence on any facility—aerospace, heavy manufacturing, defence, and more. It surfaces which accounts are actively going through real pressure, simulates your solution inside their environments, and automates true 1:1 contact-level outreach.
Configured and operated by yConversion.
From signal to conversationThe account map
Production is ramping.
Capacity expansion · Public filings · Earnings
Where does the pressure land?
Assembly workflow · Inspection · Rework
Who owns that part of the problem?
Quality engineer · Process manager · Site leader
A reason to reach out.
Their scope. Your use case. Ready for the rep.
Verified emails & numbersIndividual emailsCall briefs
An illustrative path through the system.
01 / What comes outOne solution.
One solution.
Different conversations.
Actual generated excerpts from the Matroid workflow. The agents select the angle for each person, then repeat that reasoning across the available contacts in your target accounts.
Verified emails & numbers · Matched use cases & entry points · Individual sequences · Call scripts & rep context
Product / Matroid · Computer visionResearch example / Wichita · 737 fuselage production
Actual generated output
SubjectQuick question on buried work + 737 fuselage flow
Thuan,
Saw your background in production ops / CI at Spirit. Going to ask a pretty specific question:
Ever had a leak, mis-rig, or bracket issue show up at functional test, only to find the root cause was a kinked line or loose hardware buried four stations back on the fuselage? And by then you’re into panels off, MRB, and ship-set risk.
Opening excerpt from the generated email sequence.
A real output example, built from modeled scenarios. Excerpts from the Matroid/Wichita research workflow. The situations are hypotheses for a conversation, not confirmed facility conditions or deployment results. Profile details reflect the supplied example records.
One contact shown.
Thousands mapped automatically.
This example uses computer vision. The agents build each product model from your materials, then apply it across the facilities, departments, and buyers in your market.
Built for industrial outbound.
Already opening conversations.
Calls booked at
RivianPACCARBorgWarner
Escalations generated at
HIIL3Harris
You choose the accounts. The system builds the launch.Thousands of contacts.
Thousands of contacts.
A clear angle for each one.
Targeting 30 accounts this month? The system maps their facilities and available contacts, verifies emails and phone numbers, and prepares the outreach across the whole list. Each person gets the relevant product, use case, entry point, and conversation.
01 / The people
Verified emails and phone numbers.
The system finds the available contacts, maps their facility and responsibilities, and retrieves and verifies the contact details your team needs.
02 / The outbound
Individual sequences, ready to run.
The agents match the product, use case, and entry point, then generate individual sequences across the target list.
03 / The rep context
Everything behind the conversation.
The system produces the call script, reason to reach out, relevant scenario, and contact context the rep needs to work into the account.
Built for enterprise industrial GTMDefenseAerospaceAutomotiveHeavy manufacturing
02 / One connected operating systemProduct knowledge in.
Product knowledge in.
A complete GTM launch out.
AI agents handle research, modeling, simulation, contact discovery, verification, and writing inside one connected system. The full context moves through the workflow automatically.
Find a reason
Get inside
the facility’s world.
The system maps the operating environment, programs, departments, and ownership, then simulates where the product could make a difference.
Output → Facility context & use casesFind the people.
Understand their scope.
The agents map profiles to facilities and responsibilities, then find and verify emails and phone numbers.
Output → Verified emails & numbersGive each person
a relevant conversation.
The reasoning matches the product, use case, entry point, and scenario. The writing agents produce the individual sequences and rep briefs.
Output → Emails & call briefsThe same reasoning runs across accounts, facilities, and contacts. Each person’s context determines the angle, use case, and outreach they receive.
Full context in. Best-fit scenario out.03 / Product modeling → SimulationThe agents learn your products.
The agents learn your products.
Then model their worlds.
Product docs, customer documents, messaging, sales-call transcripts, internal notes, prospect research. The system ingests the material supplied, builds a stable operating model for each product, and generates the scenarios it can apply downstream.
01 / Ingest the source material
The knowledge already in your business.
Product & technical docsCustomer docs & use casesMessaging & playbooksSales-call transcriptsInternal product notesProspect research
02 / Build each product model
Stable capabilities. Reusable scenarios.
The agents model capabilities, conditions, limits, and operating scenarios before introducing a specific prospect. That foundation stays fixed as facility context changes.
03 / Simulate the facility fit
Your solution, inside their environment.
The system combines the product model with facility and ownership models, simulates how the work could change, then layers in current pressures to select relevant use cases.
Multiple products / One operating systemA product model for every
A product model for every
solution in your portfolio.
Each product carries its own capabilities, operating model, and scenarios. Additional products can be modeled across the same target account landscape.
Product model 01Product model 02Product model 03…
Product selection / ConfigurableThe relevant product—or combination.
A product-selection stage can be configured so the reasoning matches each contact to the product, use case, entry point, and simulation scenario.
04 / What a simulation actually looks likeInspect it before
Inspect it before
the trim goes on.
The operating system combines the product model, facility environment, pressure points, and ownership model to simulate a specific working day. Here, it follows one inspector checking a fuselage window frame.
Matroid × Wichita / Pre-concealment inspectionIllustrative simulation · Adapted from system output
Product model
Matroid can flag a visible condition for a person to review.
Facility environment
737 fuselage assembly. Installed trim can hide the feature that needs checking.
Pressure context
Resolve quality findings before acceptance, while managing rework and inspection access.
Ownership model
Inspector checks the feature. Quality engineering receives the finding for review.
The scenario those inputs create
A window-frame hole needs checking before trim covers it.
Same work. Same inspector. Compare when the issue becomes visible and what happens next.
Find it after the work is covered.
Pick up the day’s jobs.
The inspector receives paper job packets for two fuselages.
The holes are already hidden.
Five window-frame holes are under installed trim. The inspector records that evidence is unavailable and requests access.
Wait for the trim to come off.
Mechanics must reopen the work before the inspection can continue.
Confirm the discrepancy.
With access restored, the inspector measures the hole, confirms it is too close to the edge, and completes the nonconformance record.
The delay carries into the shift.
Waiting for access has pushed the rest of the inspector’s work behind schedule.
Review it while access is open.
Start with the assigned units.
The inspector opens a Matroid view showing the two fuselages on the line.
Go straight to hole #17.
A visual flag arrives while the hole is still visible. The inspector goes to the location and measures the edge distance.
Record a confirmed finding.
The measurement is outside the requirement. The inspector opens a nonconformance record with the measurement and supporting evidence.
Keep the affected work accessible.
Trim installation pauses for that frame row while the responsible teams coordinate review, correction, and reinspection.
Continue to the next assigned unit.
After the required checks and handoff, the inspector moves on without waiting for trim removal.
What changed
Same inspector. Same discrepancy.
The review happens while the work is still accessible.
The handoff stays intact
The nonconformance record moves to Quality Engineering Level II for review. Qualified people retain the inspection and approval decisions.
If the image is ambiguous
The inspector still records missing evidence and escalates. The visual cue starts a review; the measurement establishes the finding.
The entry point this creates
“When trim covers a feature before inspection, how does your team get the evidence without reopening the work?”
See the contact-level outputTimes and events are illustrative, drawn from the supplied simulation. The modeled camera workflow assumes a suitable view, validated check, unit identification, and an assigned reviewer. This is an example of product fit, rather than a record of a Wichita deployment or measured results.
One agentic system. A complete GTM output.
Product knowledge, account intelligence, facility models, contact evidence, and simulations flow into the same reasoning system. Out come verified emails and numbers, matched products and use cases, individual sequences, and call briefs. yConversion configures and operates the system for your team.
A few practical details
Before we talk.
Who is this built for?
Teams selling industrial software, AI, and technical solutions into enterprise accounts across defense, aerospace, automotive, and heavy manufacturing. The system is built for use cases that change by facility, department, and responsibility level.
Is this only for computer vision or aerospace?
The Matroid/Wichita walkthrough shows one product model in one environment. The agents build each model from the relevant product knowledge, then apply it to the industries, facilities, departments, and buyers that product fits.
What does the sales team receive?
Verified emails and phone numbers, matched use cases and entry points, individual email sequences, call scripts, and the account, facility, and contact context behind each conversation. Research, modeling, verification, and writing run inside the same connected system, operated by yConversion.
Can the system model multiple products?
Yes. Each product can have its own operating model and scenario library. A product-selection stage can also be configured to match the relevant product or combination to each contact, alongside the use case, entry point, and simulation scenario.
What do you need from us?
Start with the material you already have: product and technical docs, customer documents and use cases, existing messaging, internal product notes, sales-call transcripts, and prospect research. The agents use that context to build the product models. Target accounts, signals, departments, and scope are agreed with your team during setup.
Can you run the outbound as well?
Yes. The agents automate contact research, email and number verification, angle selection, and sequence writing. The output can feed automated email sending as well as rep-led calling. Campaign operations are configured around your workflow.
Do we need to supply the account intelligence?
The research starts from external sources such as filings, earnings calls, public facility information, and professional profiles. Your team’s existing knowledge can sharpen the model, but it doesn’t have to be assembled in advance.
A complex sale needs
a clear way in.
Bring your solution and the accounts you want to reach. Let’s build the launch—from the contact universe to the conversations your team can start.
Plan your GTM launch A discovery call with yConversion.