Real People.
Real Problems.
Real Solutions.
These aren't generic testimonials. These are detailed stories of how we helped operators reclaim their time, scale their operations, and transform their businesses.
Aggregate Results Across All Cases
Real impact, real numbers
From Drowning in Admin to Running on Autopilot
How a 12-person consulting firm reclaimed 15 hours/week and prevented $180K in churn
I was spending more time updating spreadsheets than actually serving clients. Something had to change.
Sarah Chen
Founder & Managing Partner
⚠The Breaking Point
Sarah was working until midnight every Sunday. Not on client work. On admin.
She had 47 active clients spread across Notion (project tracking), HubSpot (CRM), Google Sheets (financials), and Slack (everything else). Every Sunday, she'd manually update client statuses, pull metrics for Monday's team meeting, and try to remember who hadn't heard from her team in a while.
The system was held together with duct tape and hope. When a $180K annual client slipped through the cracks (missed for 3 weeks straight), Sarah knew she couldn't scale this way. She was the bottleneck, and the business was starting to feel it.
The Pain Points:
- ✗20+ hours/week on manual status updates across 4 systems
- ✗Client relationships suffering due to slow response times (avg 2.3 days)
- ✗No visibility into at-risk accounts until it was too late
- ✗$180K client nearly churned due to missed check-ins
- ✗Founder became the operational bottleneck for everything
- ✗Couldn't take vacation without business grinding to halt
“I built this business to have freedom. Instead, I became a slave to spreadsheets. Every Sunday night felt like Groundhog Day.”
— Sarah Chen
⚡The Transformation
We started with the 7-day Map. Sarah expected consulting advice. We delivered working prototypes.
Day 1-3: We shadowed Sarah's Sunday night ritual. Watched her click through 4 different tools, copy-paste data, manually flag issues. We mapped every decision point, every data source, every pain point.
Day 4-5: Built three prototype solutions. Not slides. Not plans. Actual working software.
Day 6-7: Sarah tested all three. One emerged as the clear winner.
Then we built the real thing: A client intelligence system that syncs status updates across all her tools, generates executive reports automatically, and flags at-risk accounts before they become problems.
🔧 Technical Stack
- →Notion API → HubSpot API → Airtable (central brain)
- →GPT-4 for intelligent status analysis and risk scoring
- →Automated weekly executive reports (delivered Monday 6am)
- →Slack alerts for at-risk accounts
- →Custom dashboard showing real-time client health
Project Timeline:
Map
3 working prototypes
Engine Build
Full system deployed
Refinement
Team trained, edge cases handled
✓The Results
Hours Reclaimed
Response Time
At-Risk Visibility
Missed Follow-ups
Payback Period
Business Impact:
- ✓Sarah took her first 2-week vacation in 3 years
- ✓Client NPS increased from 42 to 67
- ✓Team morale dramatically improved (no more Sunday night panic)
- ✓Onboarded 8 new clients without adding headcount
- ✓System paid for itself in 6 weeks through time savings alone
- ✓Later added The Brain: Now texts Sarah when clients need attention, she responds with simple commands
I got my Sundays back. More importantly, I got my business back. Now I just text my system 'Who needs me today?' and it tells me. It's like having a COO that never sleeps.
— Sarah Chen, 4 months post-launch
From Inventory Chaos to Forecasting Mastery
How an 8-figure brand eliminated stockouts and saved 12 hours/week with intelligent inventory
We were either drowning in excess inventory or desperately out of stock. There was no in-between.
Marcus Rodriguez
Co-Founder & COO
⚠The $340K Problem
Marcus had a spreadsheet nightmare.
Three warehouses. Two 3PLs. Inventory data in Shopify, but also in Google Sheets, also in their 3PL dashboards, also in Marcus's head. Every week, he'd spend 12 hours manually reconciling everything, trying to figure out what they actually had.
But the reconciliation was always backward-looking. By the time he realized they were low on their hero SKU, they'd already been out of stock for 3 days. Lost sales: $340K in Q2 alone.
Meanwhile, their warehouse had $220K in dead inventory gathering dust. The forecasting was broken. The data was scattered. The COO was burnt out.
The Pain Points:
- ✗12 hours/week manually reconciling inventory across 3 systems
- ✗Stockouts cost $340K in lost revenue (Q2 2024)
- ✗$220K in dead inventory taking up warehouse space
- ✗Forecast accuracy at 58% (basically guessing)
- ✗3-day lag between stockout and realizing it
- ✗Zero visibility into what's selling vs what's sitting
“I'd spend all Saturday doing inventory. By Monday, the data was already stale. We were always reacting, never proactive. It was exhausting.”
— Marcus Rodriguez
⚡The Intelligence Layer
Marcus didn't need another dashboard. He needed a system that thinks.
We built an inventory intelligence platform that connects Shopify, both 3PLs, and their internal forecasting model into a single brain. But more importantly: it predicts.
The system doesn't just tell you current inventory levels. It tells you:
• What you'll be out of stock in 8 days
• Which SKUs are trending up vs dying
• Optimal reorder quantities based on historical data + current trends
• Dead inventory that should be liquidated
Every morning at 6am, Marcus gets a simple report: 'Here's what needs attention today.' That's it. The system handles the complexity.
🔧 Technical Stack
- →Shopify API → 3PL APIs → Airtable (central inventory brain)
- →Custom forecasting model (historical sales + seasonal trends + growth rate)
- →Automated reorder alerts with recommended quantities
- →Dead inventory detection (30/60/90 day velocity analysis)
- →Real-time Slack alerts for critical stockouts
- →Weekly executive dashboard with predicted issues
Project Timeline:
Map
Inventory flow mapped, prototypes built
Engine Build
Full forecasting system deployed
Model Tuning
Accuracy improved to 92%
✓The Results
Weekly Admin Time
Stockout Events
Forecast Accuracy
Dead Inventory
Lost Revenue (Stockouts)
Business Impact:
- ✓Saved $1.1M in lost revenue from stockout prevention
- ✓Freed up $175K in working capital by reducing dead inventory
- ✓COO went from 60hr weeks to 45hr weeks
- ✓System paid for itself in first quarter
- ✓Can now plan 60 days ahead with confidence
- ✓Warehouse efficiency up 34% (less dead inventory, better organization)
- ✓Added The Brain: System now texts Marcus 'SKU-123 will be out in 8 days, order now?' — he replies 'Yes' and it's done
We went from firefighting to forecasting. Now the system just texts me when I need to make a call. I say yes or no, it handles the rest. I actually sleep on Saturdays now.
— Marcus Rodriguez, 6 months post-launch
The Onboarding Engine That 3x'd Capacity
How a Series B SaaS company reduced time-to-value by 73% and cut churn by 23%
Our CSMs were heroes. They were also completely underwater. Every new customer felt like we were starting from scratch.
Jessica Park
VP Customer Success
⚠The Scaling Crisis
Jessica's team was drowning in their own success.
They'd grown from 50 customers to 180 in 18 months. Great problem, right? Except their onboarding process hadn't scaled. It was still manual, still inconsistent, still dependent on individual CSM heroics.
Every new customer got a 'personalized' onboarding experience. In reality, that meant: • CSMs manually scheduling 8+ calls • Copy-pasting onboarding emails • Manually tracking progress in Notion • Hoping nothing fell through cracks
Time-to-value was 45 days. Customer health was invisible until it was too late. Q1 churn hit 8.2% (ouch). Jessica's team was burnt out. And they had 40 more customers signing up this quarter.
Something had to break. Or scale.
The Pain Points:
- ✗Onboarding took 45 days on average (customer frustration growing)
- ✗CSMs maxed at 15 active customers (team at capacity)
- ✗Churn at 8.2% in Q1 (mostly new customers churning <90 days)
- ✗Zero visibility into customer health until exit survey
- ✗Inconsistent onboarding experience (depended on which CSM)
- ✗CSM burnout (60% considering leaving in employee survey)
“We were playing whack-a-mole. By the time we knew a customer was unhappy, they'd already made the decision to leave. It was soul-crushing.”
— Jessica Park
⚡The Intelligent Onboarding Engine
Jessica didn't need more CSMs. She needed to multiply the CSMs she had.
We built an onboarding engine that handles the repetitive work, so CSMs can focus on high-value relationship building.
The system:
• Automatically sequences onboarding emails based on customer progress
• Tracks completion of key milestones (not just scheduled calls, but actual product usage)
• Health score algorithm that predicts churn risk 30 days in advance
• Automated check-ins for green customers, alerts CSM for red/yellow
• CSM dashboard showing exactly where to focus attention today
The result? CSMs went from task managers to strategic advisors. The system handles the what and when. CSMs focus on the why and how.
🔧 Technical Stack
- →HubSpot + Internal Product DB → Airtable (customer brain)
- →Custom health score algorithm (product usage + engagement + milestone completion)
- →Automated email sequences with conditional logic
- →Slack alerts for at-risk customers (24hr advance warning)
- →CSM dashboard showing daily priorities
- →Weekly executive rollup of cohort health
- →Ongoing Mirror retainer for model refinement
Project Timeline:
Map
Onboarding flow mapped, health score prototype
Premium Engine
Full system + health score deployed
Mirror (Ongoing)
Monthly model refinement & optimization
✓The Results
Time-to-Value
CSM Capacity
Churn Rate
Early Warning
CSM Satisfaction
Business Impact:
- ✓Onboarded 67 new customers in Q3 (vs 28 in Q2) without adding CSMs
- ✓Churn reduction saved $1.8M in ARR retention
- ✓CSM team went from most burnt-out to most engaged in company
- ✓Time-to-value improvement increased NPS by 22 points
- ✓Health score algorithm now predicts 30 days in advance with 87% accuracy
- ✓System continues to improve with Mirror retainer
This didn't just solve a problem. It transformed how we think about customer success. Our CSMs are strategic advisors now, not task managers. And our customers can feel the difference.
— Jessica Park, 5 months post-launch
The Deal Pipeline That Never Forgets
How a $25M investment firm increased deal velocity by 60% and closed 2 'forgotten' deals
We were losing deals not because they were bad, but because we forgot to follow up. That's embarrassing and expensive.
David Kim
Managing Partner
⚠The $2.4M Opportunity Cost
David and his three partners had a world-class network. They also had a world-class mess.
Deals came from everywhere: Email introductions. Conference conversations. LinkedIn DMs. Broker relationships. But tracking them? That was chaos.
One partner used Gmail labels. Another used a Google Sheet. The third used Apple Notes. David used his memory (not scalable).
The result? Deals fell through cracks constantly. Great opportunities went cold because no one followed up. Partners would duplicate work, reaching out to the same contact twice. Or worse: they'd realize 6 weeks later that a hot deal had gone silent.
Post-mortem analysis: They lost 2 deals in 2023 (total value: $2.4M) simply because they forgot to follow up. Not because of bad terms. Not because of competition. Because of admin failure.
For a 4-person firm, that's unacceptable.
The Pain Points:
- ✗Deal pipeline scattered across 4 different systems (everyone has their own)
- ✗No shared visibility into what's actually moving
- ✗Great opportunities going cold due to missed follow-ups
- ✗Partners duplicating effort (reaching same contact twice)
- ✗Impossible to answer 'what deals are we working on right now?'
- ✗Lost $2.4M in deals due to simple follow-up failures (2023)
“We're sophisticated investors. But our deal tracking was a joke. We were losing money because we couldn't remember to send an email. That keeps you up at night.”
— David Kim
⚡The Cognitive Pipeline
David didn't need a CRM. CRMs require manual data entry. Partners don't do manual data entry.
We built a system that captures deals automatically from where they actually happen: email.
The system:
• Monitors partner emails (with permission) for deal-related conversations
• Automatically extracts key details (property, contact, stage, next step)
• Creates deal cards in central pipeline without manual entry
• Tracks last contact date and automatically reminds for follow-ups
• Surfaces 'hot deals' that haven't been touched in 7+ days
• Deduplicates across partners (no more double-reaching)
Partners never 'use the system.' The system just works in the background, keeping them organized and accountable.
🔧 Technical Stack
- →Gmail API → GPT-4 (deal extraction) → Airtable (deal pipeline)
- →Automated deal card creation from email threads
- →Smart follow-up reminders (context-aware timing)
- →Slack digest showing 'hot deals' every Monday/Thursday
- →Partner dashboard: their deals + team deals
- →Weekly pipeline review report (automated)
Project Timeline:
Map
Email flow mapped, extraction prototype
Engine Build
Full cognitive pipeline deployed
Partner Training
Team adoption, edge cases handled
✓The Results
Deal Velocity
Forgotten Follow-ups
Partner Admin Time
Pipeline Visibility
Deals Closed (Attributed)
Business Impact:
- ✓Closed 2 deals in Q1 that would have been forgotten (attributed value: $2.7M)
- ✓Deal velocity increased 60% (32 days → 20 days average)
- ✓Partners reclaimed ~6 hours/week each (no more pipeline maintenance)
- ✓Zero duplicate outreach incidents since launch
- ✓Total pipeline visibility for first time ever
- ✓System paid for itself 12x over in first quarter (from 2 attributed deals alone)
- ✓Partners now text The Brain: 'Show my hot deals' or 'What's cold?' — instant answers
This is the closest thing I've seen to a perfect tool. Now I can text it from anywhere and ask 'What deals need me?' It knows everything. We haven't lost a single deal to 'forgot to follow up' since launch. Worth every penny.
— David Kim, 6 months post-launch
The Pricing Engine That Prints Money
How a $12M scrap yard eliminated margin leakage, predicted fraud 30 days early, and added $420K to bottom line
I thought we just needed better quoting. What we got was a system that knows my business better than I do. It catches things I'd miss.
Mike Torres
Owner & Operator
⚠The Intelligence Gap
Mike had modernized—partially. He'd already moved from paper to a basic digital system. QuickBooks for accounting, a simple CRM for customer tracking, even digital scales with automatic weight capture.
But the system was dumb. It captured data without understanding it.
The real problems were invisible:
**Margin Erosion:** Mike's team quoted based on 'feel' and market rates. Some customers got great deals (too good). Others got priced out. There was no dynamic intelligence optimizing for customer lifetime value, market conditions, or material quality variance. Result: Leaving 15-20% margin on the table across high-volume accounts.
**Fraud Blindness:** Three times in 2023, contractors had mixed non-ferrous metals with lower-grade materials to game pricing. Mike only caught it when his downstream buyers complained. Each incident cost $8-12K. The system had zero fraud detection.
**Competitive Disadvantage:** When commodity prices spiked, competitors with better intelligence would adjust faster. Mike was always 24-48 hours behind market movements, losing deals or accepting bad margins.
**Customer Intelligence:** Mike had 600+ customers. Zero visibility into who was profitable, who was growing, who was about to churn. He treated a $2K/year contractor the same as a $180K/year relationship.
Post-audit: $420K annual opportunity cost from margin optimization alone. Another $140K in fraud and delayed pricing adjustments.
The Pain Points:
- ✗Pricing based on 'feel' not data (15-20% margin leakage on key accounts)
- ✗Zero fraud detection (lost $31K in 2023 from material quality gaming)
- ✗24-48 hour lag in commodity price adjustments vs competitors
- ✗No customer intelligence or lifetime value visibility
- ✗Couldn't identify at-risk high-value relationships until they churned
- ✗Manual material quality assessment (inconsistent, slow)
- ✗No predictive visibility into cash flow or inventory needs
“We had data, but it just sat there. I'd look at reports and think 'so what?' I needed the system to tell me what to do, not just show me what happened.”
— Mike Torres
⚡The Pricing Intelligence Layer
Mike didn't need more software. He needed intelligence that thinks.
We built a cognitive pricing system that continuously learns and optimizes:
**Dynamic Pricing Engine:**
The system doesn't just 'pull current prices'—it analyzes 14 data points in real-time to suggest optimal quotes:
• Live commodity market data + local competitive positioning
• Customer lifetime value score (historical behavior, volume trends, payment patterns)
• Material quality predictions based on visual + historical data
• Current inventory levels and downstream demand forecasts
• Seasonal patterns and Mike's capacity constraints
Result: Every quote is optimized for long-term relationship value, not just today's transaction.
**Fraud Detection Intelligence:**
The system learned from Mike's past fraud incidents. Now it:
• Flags suspicious weight-to-material-type ratios before payment
• Tracks customer behavior patterns (sudden material mix changes = red flag)
• Cross-references visual inspection notes with historical accuracy
• Predicts fraud risk score for each transaction
Saved Mike from 4 fraud attempts in first 6 months (total value: $38K).
**Customer Intelligence Dashboard:**
Mike sees things he never could manually:
• Customer health scores (green/yellow/red) with churn risk predictions
• Lifetime value rankings (who actually matters to his bottom line)
• Automated alerts: 'High-value customer hasn't called in 3 weeks'
• Upsell opportunities based on material patterns
The system runs his business like a data scientist, not a spreadsheet.
🔧 Technical Stack
- →Custom pricing optimization engine (proprietary algorithm)
- →Real-time commodity market intelligence + local competitor monitoring
- →Machine learning model for customer lifetime value prediction
- →Computer vision-assisted fraud detection system
- →Predictive churn analysis (30-day early warning)
- →Automated margin optimization across customer segments
- →Integration layer: QuickBooks, scale systems, SMS, market data APIs
Project Timeline:
Map
Mapped pricing logic, fraud patterns, customer behaviors
Intelligence Engine
Pricing model, fraud detection, customer scoring deployed
Model Training
System learned from historical data, accuracy optimization
✓The Results
Average Margin
Fraud Detection
High-Value Customer Retention
Pricing Response Time
Customer LTV Visibility
Business Impact:
- ✓Added $420K to annual profit through margin optimization alone
- ✓Prevented $38K in fraud attempts (first 6 months)
- ✓Saved 2 high-value customer relationships through early churn prediction ($180K retained revenue)
- ✓System identified 12 'hidden gem' customers worth nurturing (now growing 40% YoY)
- ✓Mike now focuses on strategy, not operations (reclaimed 15+ hrs/week)
- ✓Competitive pricing advantage (responds to market changes in minutes, not days)
- ✓System paid for itself in 5 weeks purely from margin improvement
I didn't realize how blind I was. Now the system tells me who to call, when to adjust prices, which deals to chase. It's like having a data scientist on staff who actually understands scrap metal.
— Mike Torres, 6 months post-launch
The Revenue Intelligence System
How a $9M HVAC company predicted equipment failures 3 weeks early, increased tech revenue 34%, and added $680K to bottom line
Dispatch was just the symptom. The real problem was we had zero intelligence about our customers, our equipment, or our actual capacity. We were flying blind.
Jennifer Ramirez
Owner & Operations Manager
⚠The Revenue Intelligence Gap
Jennifer had already modernized dispatch. She'd implemented ServiceTitan (industry-standard CRM), GPS tracking on all trucks, even automated scheduling.
But she was still leaving massive money on the table. The problems weren't operational—they were strategic:
**Reactive, Not Predictive:** Every service call was a surprise. The system tracked what happened, not what would happen. Jennifer had 2,800 customers with HVAC systems that would eventually fail—but zero intelligence about when. Result: Emergency calls (low margin, high stress) instead of planned maintenance (high margin, scheduled revenue).
**Dumb Pricing:** Techs quoted based on standard rate cards. No intelligence about customer lifetime value, seasonal demand spikes, or competitive pressure. High-value customers got the same pricing as one-timers. Peak demand periods (110°F days) priced the same as slow weeks. Margin leakage: $340K annually.
**Wasted Tech Capacity:** Jennifer's best techs (certifications, experience) were dispatched like her entry-level techs. No intelligence matching job complexity to tech capability. Result: Expensive talent on simple jobs, inexperienced techs on complex installs (callbacks, delays, bad reviews).
**Zero Customer Intelligence:** Jennifer had installed 1,200+ units over 8 years. The data sat in ServiceTitan, completely unused. She couldn't predict which customers would need replacements, who was at risk of churning to competitors, or which neighborhoods to target.
Post-audit: $680K annual opportunity cost from predictive maintenance alone. Another $340K from dynamic pricing optimization.
The Pain Points:
- ✗100% reactive service model (emergency calls, not planned maintenance)
- ✗Zero predictive intelligence about equipment failure timing
- ✗Static pricing regardless of customer value or demand conditions ($340K margin leak)
- ✗Tech-to-job matching based on availability, not capability (15% callback rate)
- ✗2,800 customers but zero churn prediction or lifetime value visibility
- ✗Missing upsell opportunities (replacement timing invisible until emergency)
- ✗Competitors with better intelligence stealing high-value accounts
“We had all the data, but it was just history. I needed to know what would happen next month, not what happened last month. That's the difference between reacting and leading.”
— Jennifer Ramirez
⚡The Predictive Revenue Engine
Jennifer didn't need better dispatch. She needed intelligence that turns data into dollars.
We built a predictive revenue system that thinks 3-6 months ahead:
**Equipment Failure Prediction:**
The system analyzed 8 years of service history (install dates, maintenance records, failure patterns) to build failure prediction models for every customer's equipment:
• Predicts equipment failure probability 3-6 weeks in advance
• Generates prioritized maintenance outreach lists
• Automates 'Your AC is due for inspection' campaigns with urgency scoring
• Converts emergency calls (20% margin) to planned maintenance (40% margin)
Result: 60% of service calls are now scheduled, not reactive.
**Dynamic Pricing Intelligence:**
The system doesn't use static rate cards—it suggests optimal pricing based on:
• Customer lifetime value score (history + predicted future revenue)
• Real-time demand conditions (110°F day = surge pricing algorithm)
• Competitive intelligence (local market rates)
• Tech utilization (slower days = discount offers to fill capacity)
Result: Margin increased 7.2% without losing customers (they price for value, not just cost).
**Smart Tech-to-Job Matching:**
System scores every job for complexity, then assigns based on:
• Tech certification match (don't send entry-level to complex commercial installs)
• Historical success rates (which techs excel at which job types)
• Learning curve optimization (give challenging jobs to techs ready to level up)
Result: Callback rate dropped 15% → 4%, tech satisfaction increased (right work for right skill).
**Customer Intelligence Dashboard:**
Jennifer now sees:
• Churn risk scores (which high-value customers are going quiet)
• Replacement timing predictions (equipment nearing end-of-life)
• Neighborhood expansion opportunities (clusters of aging systems)
• Automated upsell triggers sent to techs: 'This customer is replacement-ready'
The system transformed reactive operations into proactive revenue generation.
🔧 Technical Stack
- →Custom predictive failure models (8 years historical data training)
- →Machine learning for customer lifetime value prediction
- →Dynamic pricing engine (demand-based + customer value-based)
- →Smart tech-to-job matching algorithm (complexity × capability)
- →Automated maintenance campaign system (email + SMS)
- →Churn prediction model (30-day early warning)
- →ServiceTitan integration layer (full data sync, no duplicate entry)
Project Timeline:
Map
Historical data analysis, failure pattern identification, pricing audit
Intelligence Engine
Failure prediction, dynamic pricing, customer scoring deployed
Model Training
System learned patterns, accuracy tuning, ServiceTitan integration
✓The Results
Planned vs Emergency Ratio
Average Margin
Callback Rate
High-Value Customer Retention
Failure Prediction Accuracy
Business Impact:
- ✓Added $680K annual profit through predictive maintenance conversion
- ✓Increased margins by 7.2% through dynamic pricing ($310K additional profit)
- ✓Prevented 14 high-value customer defections through churn prediction ($420K retained revenue)
- ✓Callback rate drop saved $85K in warranty work
- ✓Tech satisfaction scores increased 32% (better job matching = more success)
- ✓Replaced 47 aging units proactively (vs waiting for emergency failures)
- ✓System paid for itself in 7 weeks from margin improvement alone
This isn't a dispatch system. It's a revenue intelligence platform. We went from waiting for phones to ring to knowing exactly who to call and when. Our business model completely changed.
— Jennifer Ramirez, 8 months post-launch
Introducing: The Brain
Your automation systems get a voice.
Text your business. It knows everything.
Ask Anything
"Which clients need attention?" "Show me today's priorities" "What's my pipeline looking like?"
Give Commands
"Order 500 units" "Schedule call with Marcus" "Send follow-up to TechCorp" — Done.
Get Proactive Alerts
System texts you: "Sarah, ClientCo hasn't heard from us in 14 days. Send check-in?"
Like having a COO in your pocket that never forgets.
Available on WhatsApp, Slack, or SMS
Notice the Pattern?
Every transformation follows the same proven process
Start with the Map
7 days. We shadow your process, build working prototypes, show you what's possible. No theory, just tangible solutions.
Build the Engine
4-8 weeks. We build the full system, train your team, handle edge cases. You get infrastructure that actually works.
Watch It Compound
The system runs. You reclaim time. Your business scales. ROI typically hits in 6-8 weeks, then compounds forever.
Ready to Write Your Own Success Story?
Every case study started with a simple 7-day Map.
$7,000. Working prototypes, not promises.
Still unsure? Learn more about our philosophy