Pipeline systems that hold up under real operating conditions.
I build the infrastructure that connects GTM activity to revenue outcomes across scaling B2B SaaS organizations.
I work with B2B SaaS companies where pipeline, capacity, and conversion need to align with revenue targets.
The focus is the operating structure behind revenue: how pipeline is created, how it converts, and how team capacity supports the plan.
Best aligned to senior operating roles and revenue planning work.
Revenue outcomes are driven by pipeline structure, team capacity, and conversion over time.
Forecast Credibility
Pipeline architecture and conversion discipline used to evaluate whether a forecast is supported by real conversion and coverage.
Revenue Math & Capacity Modeling
Headcount, ramp, and attainment are modeled to determine whether planned pipeline and bookings are supported by actual team capacity.
GTM Motion Design
Signal processing, sequencing logic, and execution design that define how pipeline is created, routed, and converted.
What this looks like in practice.
ClearCompany | $10M → $90M ARR
Supported 38 consecutive quarters of compounding growth by developing the GTM infrastructure that connected pipeline creation, conversion, and team capacity.
- Reduced SDR ramp from 90 to 30 days and improved lead to opportunity conversion from 24% to 48%
- Architected a $400M+ cumulative pipeline engine and scaled SDR org from 0 to 40+ reps
- Led hiring and onboarding across 180+ roles in Sales, Marketing, and Operations
- Inc. 5000 honoree for 9 consecutive years during this period
AI-Assisted GTM Motion | Signal Processing
Developed an outbound system to identify in-market buyers prior to inbound conversion. Focused on motion design and defining the handoff between AI automation and human execution.
- Generated a 10x pipeline return on initial investment ($60K to $600K)
- Deployed and managed 50+ AI agents integrated into the GTM tech stack
- Built signal identification and routing into outbound workflows
Systems built to answer whether revenue targets are actually achievable.
Revenue Planning Model
A structured planning system designed to evaluate whether pipeline creation, team capacity, and conversion assumptions support a given revenue target.
- Models the relationship between targets, pipeline generation, and conversion across the revenue engine
- Evaluates capacity across revenue-generating roles and how that capacity converts into pipeline and bookings
- Surfaces whether revenue targets are supported by actual operating assumptions, not surface-level pipeline metrics
Pipeline Cohort & Timing Model
A timing model built to determine when pipeline must be created to realistically close within a given planning window.
- Tracks pipeline by creation cohort and expected close timing based on historical sales-cycle behavior
- Highlights whether current pipeline can realistically convert within the periods attached to plan
- Shows when volume exists but timing breaks the likelihood of hitting bookings targets
Effective Headcount Modeling
A planning approach that treats team capacity as a time-based productivity curve rather than a static headcount number.
- Reflects ramp, peak effectiveness, and changes in output over time
- Connects hiring timing to when productive capacity actually shows up
- Produces a more realistic view of coverage and target support
The longer version.
I build the infrastructure that determines whether revenue targets are achievable, including pipeline systems, coverage models, and forecasting logic that connect activity to outcomes.
At ClearCompany, I supported growth from ~$10M to ~$90M ARR across 38 consecutive quarters by focusing on forecast credibility, capacity planning, and conversion discipline.
I work with B2B SaaS companies where pipeline quality and GTM structure are the primary constraints.
Execution reflects the structure behind it. Let's talk.
Selectively open to senior operating roles, fractional engagements, and PE portfolio work.
