AI GTM Engineering · Enterprise Data & Systems Architecture · Applied AI · Revenue Integrity · MarTech

I architect and build AI-enabled GTM, revenue and data systems that turn account intelligence into governed actions and trace outcomes through pipeline and revenue. As founder of Groundbreakers Digital, I work across founder-owned and PE-backed operators alongside enterprise B2B SaaS and ecommerce environments. I implement Python, SQL, APIs and durable control logic for identity resolution, field ownership, source authority, consent, retries, auditability and reconciliation.

The repositories below show working code, test results and recorded execution evidence, including native Salesforce, Snowflake and BigQuery acceptance as well as actual local LLM inference. The public examples use synthetic or public sample data, with execution and commercial outcome boundaries documented.

Explore by engineering focus:

• AI & GTM engineering: GTM Pipeline Orchestration · Enterprise Agent Runtime & Evaluation · Interactive SDR Account Intelligence
• MarTech & enterprise integration: Enterprise MarTech / AI Control Plane · Customer Signal & Activation · MarTech Migration Assurance
• Data & analytics engineering: Revenue Metrics Platform · First-Party Measurement Assurance · MarTech Data Reliability

Connect: LinkedIn

Explore without installing software: Open the hosted Engineering Evidence Lab. Its six interactive investigations cover AI model release assurance, governed seller actions, GTM account intelligence, failure recovery, revenue reconciliation and native CRM/warehouse evidence. Inspect the AI Model Release Assurance investigation to follow recorded Qwen3 model evaluation, shadow/canary decisions, promotion and verified rollback. The hosted site is read-only; executing fresh inference or the underlying applications requires their separate local workflows.

Featured AI release proof: Enterprise Agent Runtime & Evaluation ran actual local Qwen3-14B inference, completed 48/48 synthetic tasks per configuration, and exercised shadow evaluation, read-only canary, local promotion, controlled-fault detection and verified rollback. 79 tests passed with Windows/Linux CI. These are finite-suite local results with synthetic effects, not production claims. Explore the interactive release investigation · Executed release assessment · Self-contained evidence review

Five projects with detailed code, test results, and execution evidence.

1. GTM Pipeline Orchestration

This Python GTM application covers AI account research, fit/intent prioritization, inbound owner-routing, approved seller actions, failure recovery and qualified-pipeline measurement. The SDR account-intelligence desk exposes dated source evidence, explainable account priorities, conflicting fixture enrichment, editable outreach openings and durable local approvals/dismissals. It retains 40 actual Qwen3-14B evaluation responses across three prompt approaches, including preserved failures, plus a separate fresh source-to-inference-to-local-approval demonstration. The latest verification reports 237 passing tests and Linux, Windows and container CI.

A separate connected native Salesforce Developer Edition run took a synthetic buying signal through fresh Qwen3 inference → exact operator review → one guarded internal Salesforce SDR Task → independent readback, including lost-acknowledgment recovery. The research desk's approvals are local; native Salesforce Task execution is evidenced separately. Clay enrichment uses fixture providers, while n8n is a documented reference workflow. The 120-account, USD $3.46M qualified-pipeline scenario is synthetic and demonstrates measurement rather than client revenue.

Research architecture and evaluation · Recorded Qwen3 evaluations · Fresh operator proof · Connected native Salesforce execution · Native Task and recovery evidence · Synthetic pipeline demo · GTM verification (main)

2. Customer Signal & Activation

This Python/SQL platform connects customer signals to governed CRM decisions, preserving identity, person-level permissions, account ownership, approved actions and fulfillment-to-cash lineage. In an executed Salesforce ↔ Snowflake round-trip, it recovered an uncertain Salesforce acknowledgment without duplicating the record; independent readback confirmed the action, and CRM feedback changed the subsequent warehouse recommendation.

Its lifecycle orchestration and experimentation layer now coordinates trial onboarding, quiet-trial assistance and expansion education with durable timers, cross-journey contact arbitration, enforced treatment/holdout assignments, late-correction-safe metrics and independent HTTP receipt recovery. The implementation verifies 443 tests across the completed local suite, with successful Linux/Windows CI, nine dbt models and 13 data tests. The experiment data and commercial outcomes are synthetic; this is verified engineering behavior, not a claimed customer uplift.

Lifecycle architecture and executed proof · Experiment measurement contract · Native Salesforce–Snowflake evidence · Salesforce authorization and recovery tests

3. Enterprise MarTech / AI Control Plane

This Python/PostgreSQL control plane checks canonical identity, field ownership, consent, and approval before changes move between systems. It keeps durable execution state so a retry cannot overwrite authoritative data or repeat an uncertain downstream action. Native AWS acceptance verified PostgreSQL receipts, duplicate safety, restricted permissions and a retained-backup RDS restore reaching available. The linked execution record distinguishes observed infrastructure recovery from follow-on application validation.

AWS execution and cleanup · Recovery tests · Architecture

4. Revenue Metrics Platform

Bookings, invoices, collected cash, and ARR are not interchangeable. This platform uses SQL/dbt models, historical state and source-traceable reconciliation to keep those definitions consistent. Native Snowflake acceptance completed three fixture builds, each with 15 models and 30 tests. The larger synthetic enterprise scenario was tested locally rather than benchmarked in Snowflake.

Snowflake execution evidence

5. First-Party Measurement Assurance

Attribution breaks when browser events are lost or sales milestones are treated as collected revenue. This implementation combines consent-aware events, GTM data layers, durable offline conversion receipts and SQL/dbt reconciliation. Native BigQuery queries against Google's public GA4 sample distinguished 14 purchase events from 57 nested item rows and retained two revenue discrepancies. The Google sample and local synthetic business scenarios are documented separately.

A separate local GA4 warehouse module verifies late corrections, replay safety and incremental/full-refresh parity.

BigQuery execution evidence · Measurement verification (main)

Six more projects cover specific problems in data reliability, migrations, temporal history, AI evaluation, and platform governance.

• MarTech Data Reliability: Contract-checked ingestion, quarantine and correction handling keep invalid loads from replacing validated reporting data.
• MarTech Migration Assurance: Semantic parity, change catch-up and rollback gates expose migrations that preserve row counts but alter business meaning.
• Temporal Customer Audiences: Business-time and knowledge-time history account for late-arriving facts, corrected eligibility and auditable past decisions.
• Enterprise Agent Runtime & Evaluation: Real Qwen3-14B inference with independent outcome/effect checks, preserved rejected attempts, 48/48 tasks per paired configuration, and an executed shadow → canary → promotion → fault/rollback lifecycle. Read the acceptance evidence.
• MarTech Estate Impact: Dependency evidence and explicit uncertainty make platform retirement and consolidation decisions inspectable.
• MarTech Stack Economics: Constrained optimization tests proposed savings against cost, freshness, capacity and API quotas.

Where the projects fit across revenue systems, integration controls, enterprise architecture, and AI:

View the full-size diagram.

Enterprise Revenue Systems Lab: interactive evidence explorer · Source code

The interactive lab lets you walk through six investigations:

• AI model release assurance: Explore the recorded Qwen3 release progression—original Qwen2.5 failures, rejected 35/36 candidate, paired 48/48 finite-suite acceptance, actual local shadow/canary/promotion and controlled-fault rollback. Inspect pinned model traces, independent receipts and evidence limitations; all effects are synthetic.
• AI seller decisions: Examine a model-generated recommendation, its supporting sources, a reviewed edit, approval and recorded delivery evidence.
• Execution recovery: Trace a lost acknowledgment and worker interruption through durable workflow state and independent receiver receipts, including duplicate prevention.
• Revenue reconciliation: Explore why installed ARR, bookings, invoices and collected cash differ, with a source-backed financial bridge.
• Native Salesforce and Snowflake: Inspect the CRM-to-warehouse round trip, including source authority, lost-acknowledgment recovery, independent CRM readback and the resulting warehouse decision.
• AI account intelligence to qualified pipeline: Explore the hosted SDR Account Intelligence investigation: six synthetic research accounts, 40 recorded Qwen3 responses, evidence-grounded recommendations, rejected outputs and saved local seller-review proof. Explore the separate pipeline investigation for synthetic opportunity measurement and earlier native Salesforce evidence. The richer research did not execute through Salesforce; the separate connected Qwen3-to-Salesforce Task proof remains in the source GTM repository.

The hosted lab uses recorded synthetic evidence, not live production data. Running the underlying engines or fresh model inference requires the separate local workflows.

Enterprise platforms and business systems: Salesforce · HubSpot · Salesforce Marketing Cloud · Marketing Cloud Account Engagement (Pardot) · Marketo · Segment · Clay · LinkedIn Sales Navigator · HighLevel (GoHighLevel) · LMN · Aspire · QuickBooks · Google Analytics 4 (GA4) · Google Tag Manager (GTM) · Adobe Analytics · Tableau · Power BI · Vapi · Dialpad · Make · n8n · Workato · Zapier

Engineering, data platforms, and cloud: Python · SQL · JavaScript / TypeScript · PostgreSQL · Snowflake · Supabase · dbt · DuckDB · SQLite · FastAPI · Streamlit · Airflow · Git · GitHub Actions / CI/CD · Docker · Terraform · AWS architecture (ECS/Fargate, RDS, ECR, IAM/OIDC)

Data architecture and enterprise integration: Dimensional modeling · SCD Type 2 · Bitemporal data · Governed metrics · Data contracts · Schema evolution · ETL / ELT · Reverse ETL / warehouse-to-CRM activation · Incremental processing · Backfills · REST APIs · Webhooks · OAuth 2.0 · Canonical identity · Entity resolution · Source lineage · GTM engineering · Lead scoring and routing · SDR/AE workflow automation · Sales engagement systems · Pipeline attribution · Meta Conversions API · Google Enhanced Conversions · Offline conversion tracking · Server-side measurement

Reliability, governance, and AI systems: Source authority · Field ownership · No-Regress Logic · Stateful middleware · Idempotency · Replay safety · Retry/backoff · Exception queues · RBAC · Consent governance · Audit logging · Revenue reconciliation · Failure recovery · Release gates · OpenAI / LLM APIs · Anthropic Claude · Qwen local inference · llama.cpp · Model Context Protocol (MCP) · Prompt engineering · Tool/function calling · Structured outputs · LLM evaluation · Human approval controls

Each repository documents its code, tests, and limits. The examples use synthetic or vendor-neutral data, and I distinguish native service execution from local tests and production deployment.

Tools execute. The control layer determines what is true.