Case Studies

AI systems that changed real businesses.

Every case study here represents a real problem, a real AI solution, and real measurable impact. No vanity metrics. No vague outcomes.

$2.4M+
Value created for clients
120K+
Hours of manual work automated
50+
AI systems shipped
AI Support AgentE-commerce

AI Support Agent Deflected 78% of Tickets, Saving $18K/Month

DTC Fashion Brand · 50 employees, $12M ARR

78%
Ticket deflection rate
+78 points
< 1s
Avg. response time
From 48 hours
$18K
Monthly cost savings
vs. hiring
4.7/5
Customer satisfaction
+1.2 points

The Problem

2,000+ weekly tickets. A support team that couldn't keep up.

A fast-growing direct-to-consumer fashion brand had scaled from $2M to $12M ARR in 18 months. Their support team hadn't kept pace. They were handling 2,000+ weekly support tickets across email, chat, and social — 80% of which were repetitive questions about shipping, returns, and sizing. Response times had hit 48 hours. Customer satisfaction was falling. Hiring more agents would cost $180K+ annually and still not fix the structural problem.

The Solution

A custom support agent trained on their entire product ecosystem.

We built a context-aware AI support agent trained on their product catalog, shipping policies, return procedures, and 50,000 historical support conversations. The agent was integrated directly into Zendesk with smart escalation logic — it resolves tier-1 queries autonomously and hands off complex situations to human agents with full context, so no customer has to repeat themselves.

Custom LLM fine-tuningZendesk integrationRAG on product catalogSentiment detectionSmart escalation engine

“Our support team went from drowning to thriving. They now focus on complex, high-empathy situations — the AI handles everything routine. We wish we'd done this 18 months ago.”

Head of Operations, DTC Fashion Brand

Shipped in10 days
Lead Generation SystemB2B SaaS

Automated Lead Gen Grew Pipeline 4.3x Without Adding Headcount

HR Tech Startup · 30 employees, Series A

4.3x
Pipeline growth
In 60 days
70%
SDR time reclaimed
For actual selling
2 weeks
Time to production
From kickoff
52%
Email open rate
vs. 22% generic

The Problem

SDRs spending 70% of their time on work that doesn't close deals.

A Series A HR tech startup had a 4-person SDR team generating leads for their enterprise sales motion. The problem: each SDR was spending roughly 3 hours per day on manual prospecting, data entry, and generic outreach — tasks that were directly eating into selling time. The output was inconsistent. Personalization was superficial. Pipeline was stagnant despite headcount growing. The VP of Sales had one ask: more qualified pipeline, without adding reps.

The Solution

A fully automated top-of-funnel that runs on autopilot.

We built an end-to-end lead generation system: AI-powered ICP-matching to identify target accounts, automated data enrichment pulling from LinkedIn, company websites, and firmographic sources, hyper-personalized email sequences generated using prospect-specific context, and full HubSpot sync with automatic lead scoring. The system runs weekly, generating and qualifying a fresh set of prospects every Monday without any human input.

AI prospect identificationLinkedIn + web scrapingGPT-4o personalization engineMulti-touch email sequencingHubSpot CRM sync

“We went from 40 pipeline opportunities a month to 170 — with the same team. Our SDRs are now actually selling, not prospecting. It paid for itself in the first week.”

VP of Sales, HR Tech Startup

Shipped in2 weeks
Workflow AutomationHealthcare Technology

Automated Reporting Saved 120 Hours/Month and Eliminated Manual Errors

HealthTech Platform · 120 employees, Series B

120 hrs
Saved per month
Per team member
100%
Report accuracy
Zero manual errors
$36K
Annual savings
Ops labor cost
Real-time
Data freshness
From 3-5 days

The Problem

30 hours a week building reports that should take 30 minutes.

A Series B healthcare technology company had a 6-person operations team spending approximately 30 hours every week manually compiling executive and clinical reports from 6 different internal systems — their EMR, CRM, billing platform, analytics tool, and two custom internal databases. The process was error-prone, inconsistent, and delayed. Leadership was making decisions on data that was 3-5 days old. The team was frustrated. Turnover was becoming a risk.

The Solution

An AI-powered reporting pipeline that runs on schedule — automatically.

We designed and built an automated data pipeline that connects all 6 source systems, normalizes and validates data automatically, and generates formatted executive-ready reports on a set schedule. AI is used for anomaly detection, natural language summaries of key trends, and flagging data inconsistencies before reports are generated. Reports are delivered to stakeholders automatically — no human touchpoint required.

Multi-system data integrationAI anomaly detectionLLM report generationAutomated schedulingSlack + email delivery

“We gave our operations team their time back. They now focus on analysis and action, not data wrangling. The quality of our executive decisions has materially improved.”

Chief Operating Officer, HealthTech Platform

Shipped in3 weeks
AI Workflow AutomationFintech

AI Onboarding System Reduced Time-to-Activation by 60%

Payments Startup · 25 employees, Seed+

60%
Faster activation
7 days → 2.5 days avg
35%
Conversion improvement
On qualified leads
88%
Automation rate
Of onboarding cases
2.1x
Revenue per ops hire
Year-over-year

The Problem

Manual onboarding was killing conversion — and costing deals.

A seed-stage payments startup was losing potential customers during onboarding. Their process required manual KYB verification steps, document review, and human-in-the-loop approvals that took 3-7 business days. High-intent prospects were dropping off during the wait. Their competitors had automated this down to minutes. They needed to match that speed without sacrificing compliance.

The Solution

An intelligent onboarding pipeline with AI-assisted verification.

We built an AI-assisted onboarding system that automates document intake, uses AI to pre-screen for common KYB flags, routes edge cases to a human reviewer with a pre-populated decision context, and triggers automated communications at each step. The system reduced the human touchpoints from 12 to 2 — only genuine compliance edge cases require human review.

Document AI extractionKYB pre-screening modelSmart routing engineAutomated commsCompliance audit trail

“We stopped losing good customers to a slow process. The AI handles the routine cases instantly — our compliance team focuses on what actually needs judgment.”

CEO, Payments Startup

Shipped in3 weeks

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