Bridging software architecture and enterprise revenue. Waterloo CS graduate with enterprise engineering roots (Amex, SMART Technologies) and commercial GTM execution (Aftersell). Building autonomous systems and scaling technical pipeline.
Bio
I graduated with a Bachelor of Computer Science (Business Option) from the University of Waterloo, backed by enterprise software engineering co-ops at American Express and SMART Technologies.
I then moved into commercial execution, from transactional e-commerce into high-ticket, consultative deals focused on ROI, cash flow, and capital allocation. Along the way I scaled a marketing agency for DTC brands, learned B2B distribution for wholesale clients, and built automated GTM infrastructure.
Today that crossover is the product: I write code to build revenue systems a traditional seller can't ship, and I close deals with commercial judgment a traditional engineer rarely develops. I build autonomous lead engines that solve distribution friction and drive pipeline velocity for enterprise and high-growth commercial teams.
Case Studies
01
Autonomous B2B Lead Generation & Waterfall Enrichment Pipeline The Enterprise GTM & Waterfall Enrichment Engine.
Built for a CapEx system client in Boston, MA (in partnership with a managing partner of red light vertical booths).
How it works
Ingestion: Processes raw Outscraper gym data for targeted U.S. states.
Filtering & Tiering: Scripts built via Cursor segment accounts and score independent operators into fit tiers based on website indicators.
Waterfall Enrichment: Passes qualified data to Clay and Instantly to automate cold outreach at scale.
CursorOutscraperClayInstantlyPythonSerper
02
Enterprise Outbound Automation & Multi-Persona Pipeline Architecture An enterprise-grade outbound system built on n8n, Apify, and Airtable to automate lead discovery, fit-scoring, and multi-persona outreach across global markets.
Enterprise outbound pipelines stall when generative LLMs sit in the critical path—inflating token cost without moving reply rate.
V1 friction: high LLM token burn with minimal lift in cold response rate (~340 live sends).
V2 fix: LLM demoted strictly to Account Fit Scoring & Lead Routing, driving 90% of pipeline velocity with 0% rate-limit downtime.
Architectural Evolution (V1 → V2)
V1 Friction: LLM-generated messaging sat in the send path. Live testing across ~340 Instantly sends exposed the cost curve: heavy Google AI token burn with minimal lift in cold response rate versus deterministic persona assets.
V2 Fix: Demoted the LLM strictly to Account Fit Scoring & Lead Routing. Deterministic multi-persona assets (SE vs. AE) are selected and dispatched—no generative rewrite in the critical path—preserving reply quality while collapsing token spend and rate-limit risk.
Human-in-the-Loop Orchestration: Stages dynamic LinkedIn outreach scripts for economic buyers and peer influencers directly in Airtable, reserving manual high-touch send execution strictly for Tier 1 accounts.
Architectural Exploration (V3 Vision vs. Pragmatic Scope)
V3 Exploration: Explored collapsing the remaining manual send step into a fully autonomous multi-channel agent—Playwright/Browserless resolving gated offsite destinations and drafting dynamic LLM responses without human intervention.
Pragmatic Tradeoff: Technical validation showed that full send-path automation introduced unacceptable enterprise risk for marginal velocity gains:
Diminishing ROI: Gated redirects and modal edge cases create scraper fragility, requiring heavy maintenance for marginal time savings over a short human-in-the-loop send.
Decision: Bound automation to Data Ingestion, Account Fit Scoring, Persona Routing, and Message Staging. Preserved a ~10-second manual send step for Tier 1 outreach—delivering 90% of full-automation velocity with 100% account security and 0% rate-limit downtime.
Deterministic Execution: Runs daily at 8:00 AM EST across APAC (HK/SGP), Canada, and USA markets.
Resilience & Infrastructure: Self-hosted on PikaPods with batch timer throttling, rate-limit handling, and error-handling fallbacks.
n8nApifyGoogle AI Studio (Gemini)AirtablePikaPodsInstantlyProspeoTelegramGoogle DrivePDFShift
03
AI-Agent Pipeline & Revenue Recovery Engine The Autonomous Pipeline Generator · In Progress.
Built for Series A-D SaaS companies that leak pipeline daily: inbound signals go cold, SDRs burn hours on manual research, and high-intent accounts sit unattended.
Target outcomes: recover unworked opportunities before competitors touch them, and cut manual SDR overhead by automating signal → research → personalized outreach.
How it works
Signal Intake: RSS / funding read-pulls surface net-new buying events (raises, hiring spikes, product launches) as soon as they hit the wire.
Bottleneck Agents: AI agents score accounts, infer operational bottlenecks, and rank who is most likely to buy, so reps only touch Tier-1 recovery work.
Revenue Recovery Push: Hyper-personalized outreach sequences land in Clay/n8n with low-overhead autonomous execution attached, capturing pipeline that would otherwise expire unworked.
