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DaniilMai/README.md

Danylo Pantielieiev

GTM and Full Stack Engineer — I make company systems and data readable for AI: MCP servers over CRM and marketing data, agent workflows with evals, and the automation glue in between. A decade of full-stack engineering, now pointed at go-to-market systems.

Most GTM engineering happens inside SaaS tools and leaves no commits. This profile is where that invisible work becomes reproducible, tested systems.

Systems

System What it does Stack Status
salesforce-pardot-mcp Remote MCP server for Salesforce + Pardot: 22 tools, full MCP OAuth 2.1 Authorization Server (PKCE + Dynamic Client Registration), multi-tenant, read-only by default, 170 tests in CI Python · FastMCP · Docker · Railway In production since Jan 2026 · 20+ users
gtm-workflows Sanitized production GTM automation: three-route inbound lead orchestration, real-time demo-form grading (enrich → score → route while the visitor waits), and the website visitor play — each with architecture diagrams and honest "what broke" notes n8n · Clay · Salesforce · Pardot · Lemlist In production
gtm-semantic-layer A GTM data model an LLM can query without hand-holding: dbt + DuckDB, synthetic seeds that reproduce seven real CRM data traps, 10 golden questions with pinned answers as CI evals, and a semantic contract executed against the warehouse on every build dbt · DuckDB · SQL · Python Live — dbt build runs anywhere in ~2 min
lead-scoring-evals Should an LLM grade your inbound leads? A rules baseline (ported from production) vs LLM scorers on a labeled trap-laden dataset: per-segment accuracy with CIs, business-weighted errors, and measured dollars per 1000 leads Python · Claude API · evals Measured — 4 scorers, opposite failure profiles

How I measure AI work

An AI feature isn't done when the prompt works once — it's done when there's something that fails loudly when quality drops: fixtures, a pass rate, a cost per run, a baseline to beat. salesforce-pardot-mcp ships with 170 CI tests; gtm-semantic-layer pins ten golden questions and executes its semantic contract in CI; lead-scoring-evals is the measurement itself — baselines, confusion matrices, and cost per 1000 leads. Measured, not vibes-checked.

Stack

Python · SQL · Salesforce / Pardot APIs · MCP · n8n · Clay · dbt · Docker · Railway

Contact

LinkedIn

Popular repositories Loading

  1. gtm-workflows gtm-workflows Public

    Sanitized production GTM automation: inbound orchestration (n8n), real-time lead grading (Clay), and the website visitor play

    1

  2. salesforce-pardot-mcp salesforce-pardot-mcp Public

    MCP server for Salesforce CRM and Pardot (Marketing Cloud Account Engagement)

    Python 1

  3. DaniilMai DaniilMai Public

    GTM engineering portfolio

  4. gtm-semantic-layer gtm-semantic-layer Public

    A GTM data model an LLM can query without hand-holding: dbt + DuckDB, synthetic trap-laden seeds, golden-question evals, and an executable semantic contract

    Python

  5. lead-scoring-evals lead-scoring-evals Public

    Should an LLM grade your inbound leads? A rules baseline vs LLM scorers on a labeled dataset: accuracy, business-weighted errors, and dollars per 1000 leads

    Python