Rachel Beecham
Tech Operations · AI Enablement · Business Systems
Summary
Systems-minded operations professional combining years of business ownership and process improvement with formal AI software engineering training. Proven record of turning scattered work into repeatable systems, improving operational efficiency, and coordinating stakeholders across fast-moving environments. Builds practical AI-enabled software for real business problems and is developing hands-on skills in Python, JavaScript, TypeScript, SQL/SQLite, Git/GitHub, and LLM API integration. Targeting AI operations, technical operations, business systems, implementation, and workflow automation roles.
Core skills
Education
Maestro College — B.S. in Computer Science, Business & AI (in progress, GPA 4.0)
Embedded credential: A.A.S. in AI Software Engineering. Relevant coursework: Python Programming; JavaScript & TypeScript; Object-Oriented Programming; Data Structures & Algorithms; Software Engineering Principles. Current: Web Fundamentals.
Professional experience
- Run all recurring business and content operations as sole operator — systems, workflows, scheduling, quality control, delivery, and customer experience.
- Reduced turnaround time 30–40% by consolidating repeatable work into reusable templates, standardized processes, and streamlined systems.
- Standardized creation, review, and distribution into a single repeatable workflow, replacing ad hoc processes that produced inconsistent output.
- Coordinated documentation, logistics, supply orders, vendor communication, payroll support, and expense systems across multiple active construction sites.
- Improved onboarding efficiency 25% by standardizing documentation and intake processes.
- Served as the primary communication link between field crews and leadership, keeping multi-site work aligned.
- Managed multi-state licensing records, renewals, correspondence, and documentation workflows for a practice operating across several states.
- Cut processing delays by tracking case numbers, timelines, status, and follow-up deadlines on every active filing.
- Generated more than $500K in first-year sales as founder, running inventory, pricing, purchasing, vendor relationships, and daily operations across online and retail channels.
- Grew and maintained a private customer community of 15,000 members.
- Hired, trained, supervised, and scheduled a team of six employees.
Projects
AI Customer Inquiry Organizer
- Built a Python application that uses a large language model to convert unstructured customer messages into structured support tasks — category, priority, sentiment, order details, department routing, and a draft response.
- Designed a human-in-the-loop review workflow: no AI output is final until a person edits and approves it, with prompt-level guardrails preventing the model from committing to refunds, replacements, or delivery dates.
- Implemented a validation layer checking all model output against approved values before storage, with SQLite persistence, status tracking, and a dashboard reporting queue totals by category and priority.
Raydar
- Built an AI-assisted commerce intelligence system that structures fragmented trend, market, and product evidence into a deterministic, weighted scoring model across eight dimensions (demand, trend momentum, repeatability, customization, product fit, competition gap, confidence, and a saturation penalty).
- Separated scoring (deterministic code), interpretation (AI-assisted evidence summarization), and final decisions (human approval) — the model never gets the final vote.
- Shipped a public demo running the same interface and product logic on synthetic, anonymized data, alongside a private operating mode used to run real product decisions for my own shop.