Pisgah Forest, NC

Sam
Schuemacher

I'm looking for the right full-time fit. In the meantime, I consult selectively and run a market garden that supplies fresh produce to my community and brings me into local schools to deliver talks on the intersection of tech, sustainability, and agriculture. All of it runs on AI systems I direct: I specify the architecture, debug the failures, and let AI implement it. My edge is operational leverage that ships.

AI Systems Builder Strategy & Partnerships Operator, Schuelace Farms

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Sam Schuemacher

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About

Operator who builds
his own tools.

I'm a consultant, farmer, and AI systems builder based in the Blue Ridge Mountains of Western North Carolina. I advise financial firms on AI governance and technology adoption, run a microgreens operation, and direct the AI agent workforce that keeps both moving without the overhead of doing it by hand.

My relationship with AI isn't theoretical. I direct the systems I run my work on, from the assistant that writes my daily brief to the automation layer that keeps my businesses moving without the overhead of doing it manually. I specify the architecture and debug the failures; AI does the implementation.

What drives me: the gap between what AI can do and what most people are actually using it for. I'm interested in closing that gap, starting with my own work, and eventually helping others do the same.

Strategy & Partnerships

Microsoft Alliance & Marketplace GTM

Named Microsoft relationship manager for a 600+ client MSP, working directly with Microsoft's Partner Development Manager on alliance strategy. Now configuring Azure Marketplace offers for two early-stage AI platforms, extending Microsoft ecosystem reach across go-to-market and procurement pathways.

Operations

Schuelace Farms

Microgreens production in Pisgah Forest, NC. From seed to delivery, and AI-automated from the first harvest weight to the weekly sales report.

AI Systems

Personal Agent Workforce

A Chief of Staff agent routing to seven specialized Directors across three businesses. Thirteen scheduled jobs, zero manual monitoring.

Methodology

Align. Discover. Prove. Build. Scale.

I don't touch tooling until I understand the problem and know how success will be measured. Every AI engagement follows the same five-phase arc, whether it's a client proof of concept or something I'm building for myself.

01 / ALIGN

Align & Frame

Start with the business outcome, not the technology. Name the sponsor, the workflow, the constraints, and what "solved" looks like in measurable terms before any tool gets discussed.

02 / DISCOVER

Discover & Prioritize

Surface real candidates with the people who do the work, not just leadership. Score them on value, feasibility, readiness, and risk, then commit to the one or two worth proving out.

03 / PROVE

Design the Proof

Define success before building anything. A proof of concept without written acceptance criteria isn't a test, it's a demo waiting to disappoint someone.

04 / BUILD

Build & Validate

Prototype against real or representative data, with real users testing it, not just the model. Validate the workflow and the integration, not only whether the answer is technically correct.

05 / SCALE

Industrialize & Scale

Production is an operating-model problem as much as a technical one. Governance, ownership, training, and monitoring get built in before rollout, not bolted on after something breaks.

This is non-negotiable. Skipping alignment produces solutions to the wrong problem. Skipping proof produces confident demos that fail in production. Skipping the operating model produces a pilot that never becomes a habit. All five, every time.

Selected Builds

AI systems I use
every single day.

These aren't demos or side projects. They're production tools running on real data, solving real operational problems across my businesses.

01 / 03
Slack-Based Personal Operations Assistant

An AI agent workforce running across three businesses: a Chief of Staff (Atlas) that routes every message to one of seven specialized Directors, a Ledger watchdog that monitors everything I do daily and flags drift from my goals, and 21 scheduled jobs that keep it all moving without my attention.

View live dashboard →
Claude (Sonnet) Atlas Chief of Staff Slack Bot API FastAPI Notion API SQLite n8n Google Calendar Brevo SMTP Little Bird
7 Directors Domain-specialized advisors across growth, consulting, farm, product, finance, PM, and R&D
Daily drift check Ledger reads activity every evening, silent when on track
21 scheduled jobs Fully automated, no manual triggering
0 manual monitoring Health check + Ledger surface issues proactively

What it does

  • 7am daily brief: prioritized must-dos from Notion, calendar, and email across all projects
  • Atlas Chief of Staff: routes every Slack message to the right Director, farm → Forge, WesTech → Athena, product decision → Vector
  • Ledger watchdog: reads daily activity summary + Slack interactions vs goals every evening; Slack flag only when drift detected
  • Farm ops via Forge Director: harvest schedules, chef accounts, crop planning. Same interface, specialized context injected
  • 30-min pre-meeting packet: attendees, last commitments, open decisions, email thread context
  • Weekly Ledger review: full week analysis every Sunday, patterns, blind spots, neglected goals posted to Slack

What I directed

  • Specified an Atlas routing layer that classifies domain in a single LLM call and responds as the right Director with domain-specific context injected
  • Designed the Ledger memory system: SQLite ledger_log + Little Bird email integration, with a daily drift check and a Sunday deep review posted to Slack
  • Set goals.md as the system's north star (active projects, goals, and explicit non-priorities), and had Ledger read it on every review
  • Called for prompt caching on the Atlas system prompt to clear the 1024-token minimum on every call, cutting token cost across all Slack interactions
  • Scoped 13 APScheduler jobs: daily brief, meeting briefs, EOD capture, email sync, health check, Ledger daily + weekly, relationship digest, and more
02 / 03
Voice-Enabled Farm Operations App

A dictation-first app for logging harvest weights, planting dates, and packaging details by variety, size, and market, generating packaged-vs-sold reporting and weekly AI-assisted social media planning without manual tracking.

Voice Dictation SQLite Python / Flask Claude Email Automation
Zero Manual spreadsheet entry for harvest tracking
Weekly Automated email: sales insights + social content plan

What it does

  • Dictation-based input for harvest weights, planting dates, and packaging details, no typing, no spreadsheet
  • Generates packaged-versus-sold reporting without manual tracking
  • Automated weekly email: sales insights, operational schedules, AI-assisted social media recommendations and content
  • Visibility into inventory movement and market performance across varieties
03 / 03
Regulatory & Market Intelligence Agent

A weekly automated intelligence system built for WesTech Solutions, a Microsoft 365 governance platform for registered investment advisors. Monitors 17 regulatory, industry, and competitive sources every week, scores each finding against a 6-dimension relevance model, and generates a full strategic brief whether or not anything material happened that week. Currently being redeployed into WesTech's own Azure tenant.

Azure Functions Python Claude Notion API Azure Key Vault Brevo SMTP App Insights
17 sources SEC, RIA industry, Microsoft, and M&A activity monitored every week
6-dimension scoring 100-point model weighing regulatory materiality, RIA specificity, and GTM value
9-section brief Full strategic readout generated every week, quiet weeks included
Zero manual research Source monitoring, scoring, and drafting fully automated

Architecture

  • Azure Functions on a timer trigger (Consumption plan), running weekly against 17 monitored sources across regulatory, RIA industry, Microsoft, and M&A categories
  • Secrets (Anthropic, SMTP, Notion) held in Azure Key Vault and resolved via Managed Identity, never as plain app settings
  • App Insights and Log Analytics wired in for run-level observability
  • Delivery on two channels: an executive email brief via Brevo SMTP, and a full detail post to a dedicated Notion database

Scoring & reporting

  • 6-dimension, 100-point relevance model: Regulatory Materiality, RIA Specificity, WesTech GTM Value, AI & Automation Relevance, Cybersecurity/Microsoft, and Recency & Source Quality
  • Findings band into Material, Useful Signal, Watchlist, or Reject, with a confidence penalty for low-quality sourcing
  • Every run produces a 9-section strategic brief: executive readout, material findings, category status, GTM implications, sales/advisory opportunities, content recommendations, a standing watchlist, source methodology, and a debug appendix — present every week, not just when something material happened

What I directed

  • Directed the design of the 6-dimension scoring model and its confidence-band thresholds
  • Specified the 9-section report structure and the Claude prompt that generates it, including the no-finding-week behavior
  • Designed the Notion database schema for the weekly briefing archive
  • Directing the current migration off a personal Azure subscription and into WesTech's own Azure/Entra tenant, so the client owns the infrastructure it depends on

Experience

10 years of building
things that ship.

2026 – present

AI Strategy & Go-to-Market Consultant

WesTech Solutions

Directed the build of a multi-agent Director hierarchy and an n8n regulatory-monitoring workflow for WesTech's RIA compliance vertical, running in production today. Defined go-to-market strategy and channel positioning for an early-stage platform.

2025 – 2026

Senior Director, Solutions Engineering

Visory (formerly Swizznet)

Built and led a 5-person pre-sales function from scratch; standardized discovery and technical validation frameworks across enterprise deals, improving win rates by 30%.

2023 – 2025

Director, Product & Solutioning

Visory (formerly Swizznet)

Led cross-functional product and commercial strategy; supported the company's largest enterprise agreement at $4.2M ARR serving 600+ end customers.

2021 – 2023

Product Manager

Visory (formerly Swizznet)

Go-to-market strategy for managed services in regulated verticals; built solution frameworks that cut SE ramp time by 9 weeks across a 12-person sales org.

2019 – 2021

Senior Manager, Sales & Client Success

Visory (formerly Swizznet)

Built the client success function from the ground up; scaled a partner channel that drove 60% of new deal volume across 30 key partnerships.

2016 – 2019

Inside Sales Account Manager

Visory (formerly Swizznet)

Full-cycle sales and onboarding for SMB and mid-market accounts; built early foundations in value-based selling and translating technical capabilities for non-technical buyers.

2022

Certificate in Product Strategy

Kellogg School of Management, Northwestern University

2020

Master of Business Administration

Louisiana State University Shreveport

2013

Bachelor of Arts, Biological Anthropology

Florida Atlantic University

2025 – 2026

Guest Speaker: Technology & Agriculture

Local Schools, Pisgah Forest, NC

Delivered talks to sixth-grade science students on technology in agriculture, the future of farming, and the intersection of AI and sustainable food production. Included a live microgreens demonstration and provided take-home grow kits so every student could grow their own.

2016 – 2020

Assistant Scoutmaster

Boy Scout Troop 111

Supported troop leadership and youth development. Redesigned and redeployed the troop website and created a structured Webmaster playbook so the Scouts could manage the site going forward independently.

2014 – 2022

Active Member & Volunteer

West Palm Beach Elks Lodge

Eight years of active involvement in community service, charitable giving, and lodge programming.

How I think about AI

Practical, not performative.

Most AI hype is about what AI could do. I'm interested in what it actually does, for a specific person, with specific data, solving a specific problem.

⚙️

Build for reliability

A system that fails silently is worse than no system. Everything I build degrades gracefully. Failures are logged, fallbacks are explicit, and nothing breaks the day if a single node is down.

🔁

Close the loop

An insight that doesn't turn into an action is noise. Every output from my AI systems connects to a decision, a task, or a record. Each one can be tracked and improved over time.

📍

Context is everything

Generic AI answers are useless. The value comes from injecting the right data (your calendar, your commitments, your priorities) so the system knows what matters to you right now.

🧱

Ship, then improve

A working rough version beats a perfect spec. I build the minimum that's useful, run it on real data, and iterate from there, not the other way around.

👁️

Watch, don't just act

The most valuable agent in my system does no work. It watches what the others do (and what I do outside AI entirely) and flags when something's drifting. Doers and judges get all the attention. The watchdog is what makes the whole system learn.

How I Partner

Trust is the seat.
Everything else is a leg.

Trust is becoming the scarcest currency as AI takes over more of the systems people used to run by hand. I think of it as a stool: Authenticity, Transparency, and Follow-Through are the three legs holding the seat up. Take one away and it doesn't hold.

01 / AUTHENTICITY

Authenticity

Genuine curiosity, not performance. I listen to understand before I respond, and I don't walk into a conversation assuming I already know the fix. That assumption is exactly where trust breaks down.

02 / TRANSPARENCY

Transparency

Set expectations early, and reset them the moment there's real risk to keeping them, not at the last minute. If I don't know something, I say so, then go find the answer or the person who does.

03 / FOLLOW-THROUGH

Follow-Through

Deliver on what I promised, with ego left out of execution. Own mistakes fast and fix them to the other person's satisfaction, not just mine.

Proof, not a slogan. A partner's several hundred end customers were hitting performance issues on one specific product version, scattered complaints, hard to see as a pattern at first. Listening across dozens of them long enough to find the real cause meant discovering a vendor was quietly degrading a version it still called "supported." I brought it to the vendor directly, got their own engineers to confirm the root cause, and built a staged upgrade plan with them. Result: renewed trust with the partner, and a contract renewal that year.

Get in touch

Let's talk.

I'm open to the right opportunity. If you're building a team around AI-native operations, solutions engineering, or partner success, I'd like to hear about it. Messages go straight to my Slack.

or reach me directly

sam@samschuemacher.com LinkedIn