A working framework for how I would design, prove, and scale deliberate outbound at PACE Anti-Piracy.
Joshua (JD) Deleon · Sr. Sales Director, B2B · June 2025
PACE is a proven business that has never run outbound. Build the engine, don't invent the value.
PACE has protected software IP for nearly 40 years. 300M+ licenses deployed. Hundreds of publishers. Every commercial AAX plug-in for Pro Tools is code-signed with PACE tooling. The product works, the market trusts it, and the IP portfolio (anti-tamper, white-box crypto, licensing, code signing) is genuinely differentiated.
What does not exist yet is a deliberate outbound motion: defined ICP, repeatable prospecting, structured pipeline, and a sales process that can be measured, coached, and scaled. That is exactly what I build.
Decades of protection IP (anti-tamper, white-box crypto, licensing)
Massive deployment base (300M+ licenses, iLok)
Deep trust in pro-audio/creative-tool ISVs
New AI-relevant product surface (Fusion AI, White-Box Works)
Full executive commitment to invest in outbound
ICP definition and validation (method, not guesses)
Outbound prospecting engine (sequences, triggers, cadence)
Structured pipeline in HubSpot with exit criteria per stage
Sales process with reusable collateral and proof points
Measurement system: leading indicators before lagging revenue
Scaling path: IC first, then systematize and hire
Core belief: The first outbound seller at a company like PACE must carry a bag personally, close deals, and build the playbook from real pipeline before hiring anyone. Strategy decks do not build revenue functions; closed deals do.
Direct pattern match to what PACE needs: greenfield territory, technical buyers, AI-adjacent products.
$8M+
Career Closed Revenue
AI SaaS, data platforms, B2B services
169%
Peak Quota (Forrester)
$1.37M, Rookie of Year + President's Club
3
Territories Built from Zero
Calligo, GlobalData, Hardwire Revenue
$900K
Largest Single Deal
Healthcare enterprise, 6-mo MEDDPICC cycle
95%
Cold-Start Quota (Calligo)
No inbound, no brand, no playbook. #2 globally.
77%
Win Rate (Hardwire)
Redesigned GTM, $510K YTD 2026
I've built 3 territories from absolute zero: Calligo (North America, $0 to $1M in 16 months), GlobalData (US Consumer team, new SF office, first F50 logo in year one), and Hardwire Revenue (solo fractional CRO, $510K YTD with consecutive revenue doubling). Every role has been "walk into nothing, build the engine."
I've sold AI and ML solutions to technical buyers before the categories were trusted: Darktrace ($220K BioTech RFP, 8-month MEDDPICC cycle selling AI cybersecurity before the enterprise market accepted the category), Calligo ($900K ML modernization roadmap co-developed with a CTO and data science leadership). I know how to translate technical capability into business risk reduction language for skeptical C-suite buyers.
I'm also currently shipping production AI-native revenue platforms (RevWire, Mata Clients) using the Anthropic SDK, building 60+ specialized AI agents, and operating at the intersection of sales methodology and AI engineering. I can speak credibly to PACE's technical buyers about AI model security because I build with the technology, not just sell adjacent to it.
How I develop and test an ICP. This is method, not borrowed numbers. Every hypothesis gets validated against PACE's actual data.
Interview the team: who are the best customers today? Why did they buy?
Analyze existing customer data: verticals, deal size, cycle length
Identify look-alike profiles from the current install base
Map which products land first vs. expand later
8-12 structured customer interviews (why PACE, what else they evaluated)
Lost-deal interviews where possible
Partner/channel feedback on where PACE gets mentioned
Community signals: where do target buyers attend, ask questions?
Map competitor positioning: Digital.ai, Guardsquare, Verimatrix, Irdeto, Appdome
Identify where PACE wins on capabilities competitors lack
Find underserved segments competitors ignore or price out
Build competitive battle cards with objection handling
| Dimension | Weight | What I Score |
|---|---|---|
| Pain Intensity | 30% | Is IP protection a board-level concern or a checkbox? |
| PACE Fit | 25% | Does the threat model match PACE's capabilities (on-device, endpoint, distribution)? |
| Buying Trigger | 20% | Is there a forcing function (regulation, breach, new product, edge deployment)? |
| Accessibility | 15% | Can I identify and reach the buyer? Defined budget or buying process? |
| Deal Economics | 10% | ACV potential, expansion path, retention likelihood |
Validation sprints (2-week cycles): Select a vertical hypothesis. Run 50-75 targeted outbound touches. Measure: reply rate, meeting rate, qualification rate. After two sprints, score the vertical. If it clears threshold, build the full playbook. If not, pivot. I am testing with live signal within 30 days, not guessing for 6 months.
The vertical that maps directly to PACE's existing IP. Nobody else has claimed this position yet.
Model extraction and weight theft are live threats. Trained models represent millions in R&D. Distillation attacks, side-channel extraction, and file copying are well-documented.
Edge/on-device AI puts proprietary weights on hardware the vendor does not control. This is the exact same threat model PACE has solved for software for decades.
Regulatory and customer pressure is rising. Provenance, watermarking, and IP chain-of-custody are becoming contractual requirements.
No dominant player owns this space yet. Existing app-protection vendors are retrofitting mobile-first tools. AI-native protection is open territory.
White-Box Works
Protects keys and data on untrusted endpoints. Direct application: protect model weights, inference keys, and activation parameters on edge devices.
Fusion / Fusion AI
Runtime anti-tamper and code obfuscation. Direct application: prevent reverse-engineering of inference engines and model-serving code.
iLok + Code Signing
Secure distribution, entitlement management, provenance. Direct application: license model access per-device, per-user, per-deployment.
Positioning reframe: PACE is not adding AI as a feature. PACE already solves the problem AI vendors now have: protecting high-value IP that runs on hardware you do not control. The platform story (anti-tamper + white-box crypto + licensing + signing under one roof) is stronger than any competitor's point solution.
Parallel play: AI model security is the upside bet. The proven base that funds and de-risks it is PACE's existing strength: ISV software protection and Media & Entertainment IP security. I would run both motions in parallel from Day 1.
Named accounts with signal, entry contact, and opening angle. Built from public research; refined against PACE's internal data on Day 1.
Shipping on-device AI models via Snapdragon platforms to hundreds of OEMs; models run on hardware Qualcomm does not control
~$39B (parent); AI/ML division scaling rapidly
Sr. Director, AI Software Security
VP/SVP, Qualcomm AI Stack
Engage via AI ecosystem partnerships or developer relations. Reference iLok's scale as proof point for endpoint protection at volume.
"Your Snapdragon AI models ship onto OEM hardware you don't own. The same anti-tamper and white-box crypto that protects 300M+ software licenses can protect your model weights at the edge. Worth 20 minutes to show you how."
Leading AI video generation company; raised $141M Series D; proprietary Gen-3 models are core IP deployed via cloud and increasingly via partner integrations
~$100M+ ARR (est.)
Head of Engineering / VP Infrastructure
CTO (Cristobal Valenzuela, Co-founder)
Content via AI model security thought leadership. Direct outreach to engineering leadership referencing partnership/distribution expansion as the trigger.
"Your Gen-3 models are your moat. As you expand distribution through partnerships and on-prem enterprise deals, the question becomes: how do you protect those weights outside your own infrastructure? That's a problem we've solved for 40 years."
Shipping open-weight and proprietary models; enterprise customers deploying on-prem; raised $640M+ total; model IP is the business
~$50-100M ARR (est.)
Head of Enterprise / VP Engineering
CTO / CEO (Arthur Mensch, Co-founder)
European presence (Paris HQ) aligns with PACE's Glasgow office. Lead with on-prem enterprise deployment use case.
"When your enterprise customers deploy Mistral models on-prem, those weights leave your infrastructure. White-box cryptography can protect model parameters on hardware you don't control, the same way we protect software IP for hundreds of publishers today."
Defense AI company; autonomous drone systems; proprietary AI models deployed on contested edge hardware; $2.3B+ total funding
~$200M+ (est., contract-based)
VP Software Engineering / Chief Security Officer
CTO (Ryan Tseng, Co-founder)
Defense/gov channel if PACE has existing relationships. Lead with anti-tamper + white-box crypto positioning for contested-environment deployment.
"Your AI pilots run on edge hardware in contested environments. Model integrity and anti-tamper protection aren't nice-to-haves, they're mission-critical. We protect software IP at the endpoint for some of the most demanding environments in commercial tech."
PACE already powers AAX plug-in code signing for Pro Tools; AI-augmented features launching in Media Composer and Pro Tools; existing relationship = expansion opportunity
~$440M (public)
VP Product Engineering (existing relationship)
CTO / SVP Engineering
Warm path through existing code signing relationship. Cross-sell Fusion AI and White-Box Works for AI model protection. Fastest path to first AI deal.
"You already use PACE for AAX code signing. As you embed AI into Pro Tools and Media Composer, those models become new IP to protect. Extending Fusion to your AI features is a natural next step, and we can scope it alongside your existing integration."
Motion, positioning, and measurement. The operational backbone of outbound.
"iLok / licensing vendor for audio software"
"Protection platform for software and AI IP: anti-tamper, white-box crypto, licensing, and signing under one roof"
The reframe does not abandon the iLok/audio heritage. It expands the aperture so outbound messaging resonates with buyers outside the creative-tool ecosystem. iLok becomes proof of scale and trust, not the entire story.
Hypothesis-Driven Outbound
ICP-first, work backward. Build persona-specific cadences off trigger events. Time-boxed 2-week validation sprints. Weekly reviews to kill what isn't working and double down on what is.
MEDDPICC Qualification
Metrics, Economic Buyer, Decision Criteria, Decision Process, Paper Process, Identify Pain, Champion, Competition. Every deal scored, every gap visible, every next step earned.
Challenger Teaching
Lead with insight, not product features. Reframe the buyer's understanding of their problem. 'Your AI model weights are on hardware you don't control' lands harder than a feature list.
Multi-Threaded Enterprise
VP Engineering + CISO + CTO from day one. Never single-thread. The $900K Calligo deal had 5-8 stakeholders across CTO, data science, compliance, and finance.
Land & Expand
Lead with one product (Fusion or White-Box Works), prove value, then expand to the full protection platform. The $1.1M GlobalData deal started as a $110K pilot.
Business Case Selling
Co-develop the ROI model with the buyer. The deal is won when the champion can present the value internally without me in the room. They own the business case, not me.
Activity Metrics
Sequences sent, reply rate, meetings booked/week
Pipeline Metrics
Deals created, stage velocity, conversion by stage, avg deal size
Revenue Metrics
Pipeline coverage ratio, weighted forecast, closed-won by vertical
ICP Validation
Win rate by vertical, deal velocity by segment, expansion rate
Two distinct processes: building the engine (Pipeline Generation) and running the deals (Pipeline Management).
| # | Step | Objective | Success Criteria | Frameworks |
|---|---|---|---|---|
| 1 | Sales Planning | Target Market & Goals Alignment | Market Understanding & Actionable Roadmap | Sales PlanMarket Analysis |
| 2 | Value Proposition | Product-Market Fit Comprehension | Consistent Pitch Delivery by Industry | Onboarding GuideSales Positioning |
| 3 | Buyer Alignment | ICP & Target Personas Finalized | Validated Targets & Sales Process Roles | Persona ProfilesWin/Loss Analysis |
| 4 | Solution Alignment | Value Positioning Comprehension | Messaging Aligned to Buyer Journey | Value FrameworkSales Battlecard |
| 5 | Account Planning | Segmented Target Lists per ICP | Account Scoring & Prioritization | Account MappingAccount Plan |
| 6 | Outbound Cadence | Account Lists & Planning | Data-Driven Activity Targets | Quota AttainmentPipeline Productivity |
| 7 | Prospect Targeting | V1 Messaging Templates | ICP-Aligned BD Frameworks | Outbound PlaybookObjection Handling |
| 8 | Measure & Refine | Analytics Process Established | Weekly Reviews Calendared | Activity PlanningOutbound Analysis |
| # | Stage | Objective | Exit Criteria | Frameworks |
|---|---|---|---|---|
| 1 | Identify | Find & Frame High-Cost Problem | Deep Discovery / Qualification Confirmed | Discovery RoadmapCommunication Plan |
| 2 | Problem Validated | Multi-threaded Input & Validation | Problem Statement & Metric Impact Confirmed | Demo PlanningDeal Qualification |
| 3 | Exec Sponsor Secured | Key Exec Project Sponsorship | Exec Buy-In & Change Evaluation | Business CaseExecutive Summary |
| 4 | Approach Confirmed | Agreement on Approach Across Committee | Committee Decision to Buy Confirmed | Mutual Action PlanSolution Proposal |
| 5 | Vendor Confirmed | Provider of Choice Confirmed | Competition Ceased & Full Scope Aligned | Negotiation & ClosingProof of Value |
| 6 | Timeline on Track | Compelling Event Driving Go-Live | Go-Live Tasks Anchored to Outcome | Pipeline ForecastingFuture Press Release |
| 7 | Paperwork Signed | Commercial Terms Signed | Agreement & Onboarding Plan Executed | Contract TemplatesAccount Handoff |
| 8 | Assess & Measure | Deal Self-Evaluation & Shared Insights | Opportunity Learnings Documented | Deal ReviewPipeline Analysis |
Learn. Prove. Scale the bet.
Product immersion
Hands-on with Fusion, White-Box Works, iLok platform, code signing. Understand the technical buyer's evaluation criteria and demo flow.
8-12 customer interviews
Structured conversations: why did they buy? What triggered the purchase? What nearly stopped it? Who else did they evaluate?
Internal alignment
Build relationships with engineering, product, support, leadership. Map the sales motion that exists today (inbound, partner, word-of-mouth).
Competitive battle cards
Initial cards for Digital.ai (Arxan), Guardsquare, Verimatrix, Irdeto, Appdome, Promon. Positioning, pricing intelligence, objection handling.
ICP hypothesis drafted
Two vertical hypotheses (AI model security + ISV/M&E expansion) with scoring rubric. Ready for validation sprints.
HubSpot stood up
Pipeline stages with exit criteria, required properties, activity tracking, outbound sequence measurement, basic reporting dashboards.
Deliverables
First outbound sprints launched
50-75 targeted touches per vertical per 2-week cycle. Measure reply rate, meeting rate, qualification rate. Kill what doesn't work.
3-5 qualified opportunities
Net-new pipeline from outbound. At least one deal per vertical hypothesis, providing real signal on ICP accuracy.
Messaging iteration
Refine sequences based on what gets replies, what objections surface, what resonates. A/B test subject lines and value hooks.
Sales collateral built
First-touch email templates, technical one-pagers per product, case study briefs (from customer interviews), competitive comparison sheets.
ICP hypotheses scored
Which verticals are converting? Where is the pain strongest? Data-driven adjustment to targeting and messaging.
Weekly pipeline review established
Cadence with leadership. This is new for the org; model what 'good' looks like for pipeline inspection and forecast.
Deliverables
8-12 qualified opportunities
Pipeline growth from validated verticals. First deal(s) advancing to technical validation or business case stage.
Playbook codified
ICP, sequences, objection handling, competitive positioning, demo flow, proposal templates. The artifact that makes this repeatable.
Hiring plan drafted
Based on validated ICP and pipeline data: profile for hire #2 (BDR vs. AE depends on what the data says), ramp timeline, quota model.
Revenue forecast built
Bottom-up model for Year 1 based on actual pipeline velocity, conversion rates, and deal sizes observed in the first 90 days.
Cross-sell/upsell opportunities mapped
Identify expansion plays from existing customer base conversations. AI protection upsell to current ISV customers.
90-day readout to CEO
Present findings, pipeline, ICP validation, and forward plan. Transparent assessment with data behind every recommendation.
Deliverables
90-Day Success Definition: 8-12 qualified opportunities in pipeline from net-new outbound, with at least 2 advancing past technical validation. A validated ICP with data behind it. A documented, repeatable playbook. A data-informed recommendation on team expansion and quota model. One deal in late stage or closed.
Prove it myself first. Then systematize. Then lead.
Carry a personal bag; source, prospect, close
Build the ICP, pipeline, and sales process from live deals
Establish cadence and measurement
Generate the first wins that fund the next hire
Credibility comes from doing the job, not delegating it.
Document every part of the motion into a repeatable playbook
Hire #2 (BDR or AE; data decides)
Onboard with the playbook, not tribal knowledge
Maintain personal quota while enabling the new hire
The playbook is the product of Phase 1.
Shift from majority-IC to majority-leader as the team produces
Own forecast, pipeline review, coaching, and hiring
Continue carrying strategic accounts personally
Build the case for team expansion based on pipeline data
The transition is gradual and data-driven, not a cliff.
Critical principle: I do not hire before I have a playbook worth handing someone. Every hire before the motion is proven is a gamble. Every hire after it is proven is a multiplier.
Defining quota, OTE, and ramp bottom-up. PACE has no internal benchmark for outbound; here's how we build it together.
| Input | How We Get It | What It Informs |
|---|---|---|
| Average deal size | Existing contracts + early outbound pipeline by month 3 | Quota sizing, pipeline coverage |
| Sales cycle length | Historical inbound data; adjust 1.5-2x for cold outbound | Ramp period, revenue timing |
| Win rate | Benchmark 15-25% for net-new enterprise; refined by month 4 | Pipeline coverage ratio (3-4x) |
| Market comp data | Security/dev-tools SaaS, Bay Area benchmarks | OTE range, base/variable split |
| Ramp | 6-9 months realistic for greenfield technical enterprise | Quota relief timeline |
Based on comparable app-protection and licensing vendors, I would expect PACE to price along one or more of these models:
Per-Application
Protection applied per software title or AI model. Scales with publisher's product portfolio.
$15K-$75K/yr per app
Platform License
Full-stack access to protection suite. Higher ACV, longer sales cycle, bigger commitment.
$100K-$300K+/yr
Usage / Volume
Tied to deployment scale (licenses issued, devices protected, models signed).
Tiered by volume
In a 4-9 month cycle, lagging indicators (revenue) arrive too late to steer. Track these from Day 1.
| Indicator | Measures | Healthy Range |
|---|---|---|
| Sequences launched/week | Is the engine running? | 15-25 new accounts |
| Reply rate | Is messaging resonating? | 8-15% |
| Meetings booked/week | Is outbound converting? | 3-5 by month 3 |
| Discovery-to-qualified rate | Is ICP targeting accurate? | 40-60% |
| Stage velocity | Are deals moving or stalling? | <30 days per stage |
| Multi-threading score | Single-threaded risk? | 2+ contacts per deal |
Click any question to see why I'm asking it.
Which of PACE's products do you see as the lead offering for outbound, and has that changed recently with the AI push?
What percentage of current revenue comes from the pro-audio/creative-tool ecosystem vs. other verticals?
Today, when a new customer buys PACE, how did they find you? What does that journey look like?
How technical does the sales conversation get before engineering needs to be involved? What does a typical evaluation look like?
When you lose a deal (or a prospect chooses not to buy), what are the most common reasons?
What does the current team look like on the commercial side? Who would I be working most closely with day-to-day?
What does success look like to you at 6 months and 12 months? How are you thinking about the investment horizon for outbound?
How far along is the Fusion AI positioning internally? Is there existing customer interest, or is this a bet ahead of demand?
How does PACE price today? Per-application, platform license, per-seat? Is there flexibility to package for new verticals?
Disclaimer
All ICP definitions, market sizing, deal economics, pricing hypotheses, and conversion benchmarks in this brief are informed estimates, not validated facts. They are drawn from my experience building outbound at comparable companies (technical enterprise sales, security-adjacent products, greenfield territories), but they have not been tested against PACE's internal data. Every assumption would be validated, refined, or discarded in the first 60 days.
I believe this honesty is a feature, not a limitation. Anyone who presents confident market numbers for a motion that has never been run is guessing. I would rather show you how I think and test than pretend I already know.