Early Out (EO) Slack Bot

Full-Stack Phased Workforce Release System

A production Slack bot that automates the entire Early Out lifecycle for a 24/7 contact center, built on Railway with Flask, PostgreSQL, and APScheduler.

$624K+Annual Labor Savings

21xROI vs. Manual

~$5/moInfrastructure Cost

Overview

The bot runs the whole Early Out day on its own. A plan goes up via web dashboard or API, team leads review it with interactive exclusion buttons, agents get phased DMs with Accept/Decline/Later buttons, every response lands in a management channel as it happens, and the day closes with automated end-of-day reporting plus CSV export and email delivery.

Each phase supports three strategy tiers (Aggressive / Moderate / Conservative) that control which tiers auto-release vs. require manual approval via slash commands.

Daily Timeline

Plan uploaded the day before via web dashboard. At 7:00 AM the bot posts the agent list to #team-leads with ✖ Remove buttons. At 9:00 AM the exclusion window locks and the final count is posted. From 9:45 AM to 5:45 PM DMs fire 15 min before each phase release (5 phases × 3 tiers), and agents tap Accept, Decline, or Later (with +1hr/+2hr/Next Phase deferral). Every response is logged to #eo-management as it lands. At 7:00 PM the EOD summary and CSV are emailed automatically.

Slash Commands

/eo-status /eo-approve 1 2 /eo-cancel /eo-exclude

PythonFlaskSlack BoltPostgreSQLAPSchedulerRailwayREST APIGunicorn

EO

EO Bot

● Phase 2 Active • 3 of 5 phases complete

#team-leads • 7:00 AM

📋 EO Review • April 1
12 agents eligible • Remove conflicts before 9:00 AM

Phase 1 🟢 AGGRESSIVE • 4 agents

✖ M. Rivera (L1) ✖ K. Thompson (L1) ✖ D. Kim (L2) ✖ R. Chen (L1)

✅ Review complete. 1 agent excluded. 11 agents remain eligible. DMs begin at 9:45 AM.

DM to Agent • 9:45 AM • Phase 1, Tier 1

Hey Maria! 👋

You're eligible for Early Out today.
🕐 Your shift: 7:00 AM to 5:00 PM • 🚪 Leave at: 10:00 AM • ⏳ EO hours: 7.0 hrs

✅ Accept EO ❌ No Thanks ⏰ Later EO

📊 End of Day EO Summary • 7:00 PM

7/11

Accepted

64%

Accept Rate

44.5

Hours Recovered

📧

CSV Emailed

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Workforce Intelligence Suite

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AI Boarding Agent

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AI Workforce Intelligence Suite

16-Module Analytics Platform Built on Claude AI

A workforce intelligence suite that replaced multiple commercial tools. Each module is a custom Claude skill with structured prompts, data parsers, and automated output generation.

$156K+Annual Savings

16Modules

21xROI Improvement

Overview

This suite took over the jobs of several commercial tools we were paying for. It spans the full operational lifecycle, from daily performance reporting and forecasting through capacity planning, agent coaching, and partner SLA monitoring.

How It Works

Each module ingests raw data exports (CSV, XLSX) from Genesys Cloud and internal systems, applies domain-specific analysis logic via Claude skills, and produces formatted reports, dashboards, and clear recommendations, replacing manual processes that used to take hours.

Claude APIClaude CodeCustom SkillsPythonGenesys CloudErlang CExcel Automation

Workforce Intelligence Suite

Service Level

84.2%

Forecast Accuracy

88.6%

FTEs Scheduled

47

Cost Avoidance

$52K

Module Coverage

DPR Analysis

95%

WCS Trends

88%

Capacity Plan

82%

Agent Coaching

90%

Partner SLA

76%

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Storm Watch

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Early Out (EO) Slack Bot

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AI Boarding Agent

Conversational AI Handling 50% of Peak Call Volume

An AI-powered boarding agent that automates the highest-volume call type in the contact center using structured conversation flows, intelligent triage, and real-time decision trees.

50%Peak Calls Automated

$102KProjected Annual Savings

24/7Availability

Overview

Boarding is the single highest-volume call type in the center, so that's the one I handed to an AI agent. It works through the interactions that used to tie up dedicated human reps, which frees them for the calls that actually need a person.

How It Works

Incoming calls matching boarding criteria are routed to the AI agent, which guides callers through structured workflows, collects required information, validates data against business rules, and either resolves the interaction or intelligently escalates to a human agent with full context.

Claude AIPrompt EngineeringConversation DesignTriage LogicGenesys CloudREST APIs

AI

AI Boarding Agent

● Active • handling calls

Welcome to our boarding support line. I can help you get set up today. Could you provide your account number or the name on the account?

AI Agent • 10:42 AM

Account #48291-KC. New install for Johnson residence.

Caller • 10:42 AM

I found account #48291-KC. I see this is a new residential installation in the Kansas City service area. Let me verify a few details and get this boarding process started.

✅ Account located
✅ Service area confirmed
⏳ Verifying installation schedule...

AI Agent • 10:43 AM

Installation is confirmed for Thursday 3/27 between 9AM-12PM. I've completed the boarding checklist and activated the account. Is there anything else you need help with?

AI Agent • 10:43 AM • Resolved without escalation

1:12

Avg Handle Time

87%

First-Contact Resolve

342

Calls This Week

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Early Out (EO) Slack Bot

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Automated Reporting Pipeline

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Automated Reporting Pipeline

End-to-End Data Pipeline Replacing Manual Processes

An automated reporting pipeline that ingests data from Genesys Cloud and internal systems, then generates formatted reports, dashboards, and Slack notifications without human intervention.

85%Time Reduction

$50-100KSoftware Cost Avoided

100%Hands-Off Daily Runs

Overview

This pipeline pulls from Genesys Cloud, internal databases, and operational systems, then turns all of it into formatted reports, dashboards, and Slack notifications with nobody in the loop. What used to eat 2+ hours of manual work every day now finishes in minutes, and it's more accurate and consistent on top of that.

How It Works

Scheduled Python scripts pull data via REST APIs, process and normalize it through custom parsers, generate analysis through Claude AI skills, format outputs into Excel reports and HTML dashboards, and push summaries to Slack, creating a fully automated operational intelligence loop.

PythonFlaskREST APIsSlack BotPostgreSQLRailwayClaude AIExcel Automation

Automated Report Pipeline • Daily Run

Pipeline Status • April 1, 2026 06:00 AM

✓

Data Ingestion

Genesys Cloud API → 3 endpoints polled

12s

✓

Parse & Normalize

CSV/JSON → structured DataFrames

8s

✓

AI Analysis

Claude skill processing → insights generated

34s

✓

Report Generation

XLSX + HTML dashboard created

6s

✓

Distribution

Slack notification + file delivery

2s

62s

Total Runtime

0

Errors

5

Reports Sent

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WFM Forecaster & Scheduler

Full-Stack Workforce Management Web App

A complete workforce management platform built from scratch on Flask, SQLAlchemy, and Erlang C, covering forecasting, scheduling, intraday monitoring, KPIs, and a full RBAC compliance layer.

5Modules

5RBAC Roles

30-minInterval Granularity

Overview

I built this WFM platform from the ground up to cover the full lifecycle: demand forecasting with weighted moving averages and healthcare-specific demand drivers, Erlang C-driven schedule generation with PTO capacity checks and shrinkage configuration, real-time intraday monitoring with adherence tracking, a KPI dashboard with auto-generated anomaly insights, and a compliance layer with role-based access control and audit logging.

How It Works

Historical CSV data feeds a weighted moving average engine that generates interval-level volume forecasts. The forecast feeds Erlang C calculations to determine minimum staffing per interval. The scheduler assigns agents to shifts by seniority, respecting PTO windows and shrinkage deductions. Intraday view compares live staffing vs. demand. Reports flag anomalies using 4-week trailing averages. All actions are audit-logged with RBAC enforcement across 5 roles (Manager, Analyst, RTA, Leadership, Auditor).

Key Features

MAPE/WAPE/Bias accuracy tracking, demand driver overlays (multiplier + additive + weekly curves), FTE & cost estimation, PTO capacity guardrails with minimum staffing thresholds, contingency plan activation, and auto-insight generation.

PythonFlaskSQLAlchemyPostgreSQLErlang CFlask-LoginFlask-MigrateChart.jsHTML/CSS/JS

WFM Forecaster & Scheduler

Forecast

Schedule

Intraday

Reports

RBAC

Forecast MAPE

10.5%

Peak Agents

52

Service Level

80/30

Shrinkage

24.5%

30-Min Interval Forecast vs. Staffed

━ Forecasted Volume

╌ Agents Staffed

Erlang C • Peak Interval

52

Agents Req

82.1%

Service Level

0.74

Occupancy

38.4

Erlangs

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LLM Support Triage

Knowledge-Base-Grounded LLM Triage & Evaluation

A daily parser that restructures an internal support knowledge base and injects it into an on-prem LLM triage gateway, paired with a rigorous offline harness that A/B-tests prompts and KB shapes against tens of thousands of ground-truth tickets.

39K+Tickets Evaluated

108Providers' KB Automated

67%Offline Accuracy (A/B)

Overview

The goal was simple: get techs to trust the LLM's answers more often. So I built a daily pipeline that pulls an internal knowledge base via API, restructures it per-provider, and feeds it into the existing on-prem triage gateway (Perl + Ollama, llama3.1:8b), then measured whether it actually moved the needle. The bar to clear: lift acceptance of LLM answers from about 40% toward 60%.

The Parser

The KB is organized by topic, not by provider, so the parser crosswalks topic/queue content to each provider, transforms HTML into the clean <documents> XML the gateway already expects, and gates every provider to the services it actually offers. It covers 108 of 109 active providers. Testing two KB "shapes" showed a compact per-provider delta beat the full document dump. It measurably reduced hallucination and over-escalation on the 8B model.

Prompt Engineering & Evaluation

I pulled the production prompt verbatim, wrote a handful of variants, and replayed them through a faithful offline copy of the gateway's 3-sample consensus and confidence-threshold routing against roughly 39K ground-truth-labeled tickets. The balanced winner raised offline accuracy-of-presented to 67%, fixed the worst-performing segment, and cut over-escalation, all without reducing answer volume.

Key Finding

The 8B model itself is the accuracy ceiling (about 70-75% practical max, 75% self-consistency), not the knowledge base. Bigger gains would need a stronger reasoning model. The jump from 40% to 60% acceptance is the real goal, and I'd only call it proven after a production A/B.

PythonPerlOllamallama3.1:8bPrompt EngineeringLLM EvaluationREST APIPandasXML

LLM Triage Gateway • llama3.1:8b

Incoming ticket · Email

"My email stopped sending this morning. Do I need to change my outgoing server settings?"

<knowledge_base> compact KB injected · Email · provider-specific deltas (server / webmail / quota)

Sample 1

REFER → PROVIDER

Sample 2

REFER → PROVIDER

Sample 3

TROUBLESHOOT

3-sample consensus · confidence 0.82 ≥ threshold

REFER_TO_PROVIDER

✅ Grounded in KB · presented to tech (not routed to human review)

67%

Offline Accuracy

10%

Over-Escalation

39K

Tickets Tested

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Automated Reporting Pipeline

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WFM Forecaster & Scheduler

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Storm Watch

Real-Time Severe-Weather Operations Map

A live weather-ops dashboard that overlays NEXRAD radar, NWS warning polygons and SPC convective outlooks onto a nationwide network of monitored sites, so the operations team can immediately see which locations are under active severe weather.

260+Monitored Sites

48States / Regions

5-minLive Refresh Cycle

Overview

It's a single-page operations map. Every monitored site sits on a dark Leaflet/CARTO basemap, with live government weather data stacked on top: animated NEXRAD base reflectivity, active NWS warning and watch polygons, and SPC Day 1-3 categorical risk areas. The whole thing exists to answer one question at a glance: which sites are at risk right now?

How It Works

A ray-casting point-in-polygon routine tests every geocoded site against each live alert and SPC Day-1 polygon (handling both Polygon and MultiPolygon geometries with holes). Matches surface in two ranked side panels ("In Active Warning" and "In SPC Day-1 Risk") with click-to-fly, type-ahead search by name, city or site ID, hazard filters (convective, flood, winter, wind), and on-demand point forecasts pulled per location.

Live Data Sources

NWS Active Alerts API (GeoJSON) · SPC Convective Outlooks (NOAA ArcGIS MapServer) · NEXRAD N0Q radar (Iowa State Mesonet WMS) · NWS point-forecast API. Everything is fetched client-side and auto-refreshes every 5 minutes, with graceful failure handling so the map stays usable if a feed is down.

JavaScriptLeaflet.jsNWS APISPC OutlooksNEXRAD / Iowa MesonetGeoJSONPoint-in-PolygonCARTO

Storm Watch • Weather Ops Console

⚠ In Active Warning (3)

Site #1042S-Central KS TOR WRN

Site #0815Central KS SVR TSTM

Site #2207E Missouri FLOOD

◆ In SPC Day-1 Risk (5)

Site #1180E Kansas ENH

Site #0337N Kansas SLGT

NEXRAD radar NWS alerts SPC Day-1 Monitored sites

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WFM Forecaster & Scheduler

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Workforce Intelligence Suite

I design, build & ship
production AI systems

Phased Workforce Release System

16-Module Analytics Platform on Claude AI

Conversational AI

End-to-End Automation

Knowledge-Base-Grounded LLM Triage & Evaluation

Full-Stack Workforce Management App

Real-Time Severe-Weather Ops Map

Let's build something that pays for itself.

AI & Automation

Programming

Workforce Management

Analytics & Reporting

Have a project or a role in mind?

Full-Stack Phased Workforce Release System

Overview

Daily Timeline

Slash Commands

16-Module Analytics Platform Built on Claude AI

Overview

How It Works

Conversational AI Handling 50% of Peak Call Volume

Overview

How It Works

End-to-End Data Pipeline Replacing Manual Processes

Overview

How It Works

Full-Stack Workforce Management Web App

Overview

How It Works

Key Features

Knowledge-Base-Grounded LLM Triage & Evaluation

Overview

The Parser

Prompt Engineering & Evaluation

Key Finding

Real-Time Severe-Weather Operations Map

Overview

How It Works

Live Data Sources

I design, build & shipproduction AI systems

Phased Workforce Release System

16-Module Analytics Platform on Claude AI

Conversational AI

End-to-End Automation

Knowledge-Base-Grounded LLM Triage & Evaluation

Full-Stack Workforce Management App

Real-Time Severe-Weather Ops Map

Let's build something that pays for itself.

AI & Automation

Programming

Workforce Management

Analytics & Reporting

Have a project or a role in mind?

Full-Stack Phased Workforce Release System

Overview

Daily Timeline

Slash Commands

16-Module Analytics Platform Built on Claude AI

Overview

How It Works

Conversational AI Handling 50% of Peak Call Volume

Overview

How It Works

End-to-End Data Pipeline Replacing Manual Processes

Overview

How It Works

Full-Stack Workforce Management Web App

Overview

How It Works

Key Features

Knowledge-Base-Grounded LLM Triage & Evaluation

Overview

The Parser

Prompt Engineering & Evaluation

Key Finding

Real-Time Severe-Weather Operations Map

Overview

How It Works

Live Data Sources

I design, build & ship
production AI systems