AI Implementation & Business Process Automation

Tanya Omelchuk

Process clarity · workflow mapping · automation logic · AI-assisted systems

I help teams identify where workflows need documentation, where automation belongs, and where AI can support analysis, coordination, and decision-making.

About

I work in AI implementation and business process automation, with a focus on helping teams identify where automation or AI belongs inside real operations.

My work sits at the intersection of process clarity, workflow mapping, deterministic automation, and AI-assisted decision-making. I am especially interested in the boundary between workflows that need better documentation, workflows that can be automated through clear rules, and workflows where LLMs can support judgment, classification, analysis, or coordination.

I have built and documented workflow systems using Airtable, Make, Zapier, APIs, and AI-assisted development tools. I also prototype with n8n, Claude Code, OpenAI, and Anthropic tools.

My current focus is on practical AI-enabled workflow systems: bounded, monitored processes that coordinate tasks across tools while preserving auditability, reliability, and human oversight.

Portfolio

Project

Manufacturing Production System

MES-style workflow architecture for production visibility, task sequencing, and operational control

Business problem

A small manufacturing workshop relied on paper notes, printed order sheets, and manual shop-floor tracking. As order volume increased, the team lost visibility into order status, production stage, task ownership, and sequential handoffs.

What I built

Designed and implemented a relational Airtable production system connecting orders, order lines, production batches, production tasks, and production blueprints into one operational workflow.

System logic

Blueprint-driven production logic, automated task generation, sequential dependency enforcement, and progress tracking across batch, order line, and order levels.

Tools

Airtable, relational database design, Airtable Automations, operator interfaces, workflow architecture.

Outcome

Created a structured production view where managers can monitor order progress and floor operators can follow a dependency-enforced task queue without manual status chasing.

Read Case Study PDF

Project

Production Workflow Diagnostic

Agent-readiness assessment for a high-volume production environment

Business problem

A production environment was experiencing recurring rework, handoff delays, unclear ownership, and visibility gaps across departments. These issues reduced process reliability and made automation risky without stronger workflow foundations.

What I did

Mapped the order-to-production workflow, documented operational bottlenecks, and identified where missing data standards, unclear status ownership, and informal handoffs were blocking reliable automation.

Diagnostic logic

Separated the workflow into documentation gaps, deterministic automation opportunities, and future AI / agent-readiness requirements.

Tools / methods

Workflow mapping, SIPOC-style analysis, stakeholder interviews, operational observation, Google Workspace, JobPro / FileMaker context.

Outcome

Clarified where process documentation, data normalization, ownership rules, and visibility systems must come before responsible AI or agentic automation.

Project

Slack Bot for Real Estate — Buyer Preference Collection

Working Slack MVP for structured buyer intake

Business problem

Real estate buyer intake is often handled through scattered conversations, repeated follow-up messages, and incomplete preference notes. This slows qualification, weakens handoffs, and makes it harder for agents to maintain consistent buyer profiles.

What I built

Built a working Slack bot that collects buyer preferences through an interactive modal and sends a structured confirmation message after submission.

Core functionality

The bot collects search area, price range, bedroom requirements, and must-have notes. It supports a /buyer-profile slash command, interactive modal submission, confirmation DM, hello command, and error handling.

Technical implementation

Implemented with Node.js, Express.js, Slack Bolt / Web API, HTTP endpoints, environment configuration, ngrok webhook tunneling, and HMAC-based Slack request signature verification.

Outcome

Created a functional buyer intake workflow that turns informal buyer conversations into structured preference data inside Slack.

View GitHub Repository

Project

AI Lead Triage + Autoreply Agent

Zapier workflow for inbound lead classification and response drafting

Business problem

Inbound leads arrive with different levels of urgency, intent, and clarity. Without triage logic, teams spend time manually reading messages, deciding priority, and drafting repetitive first responses.

What I built

Built a Zapier-based AI workflow that reads inbound Gmail messages, detects intent, classifies leads into hot, warm, or cold paths, logs the lead in Google Sheets, sends Slack notifications, and creates tailored Gmail draft replies.

Workflow logic

Gmail intake → text formatting → AI intent detection → lead classification → path routing → Google Sheets logging → Slack notification → Gmail draft generation.

Tools

Zapier, Gmail, Google Sheets, Slack, OpenAI API.

Outcome

Created an AI-assisted lead handling flow that prioritizes inbound leads, standardizes follow-up, and reduces manual response drafting.

AI Lead Triage Agent PDF

Project

Appointment Reminder + Review Flow

Workflow automation prototype for appointment follow-up and review collection

Business problem

A service business needed a more consistent way to reduce missed appointments and collect customer reviews without manual follow-up.

What I built

Built a functional automation workflow that sent SMS reminders before appointments and email review requests after service completion.

Workflow logic

Appointment intake → scheduled SMS reminder → post-appointment email follow-up → review request → Google Sheets tracking.

Tools

Make, Zapier, Google Forms, Google Sheets, Telnyx, Gmail.

Outcome

Delivered a working prototype and clarified the operational requirement for reliable automation: standardized appointment and calendar data.