Case Studies

Built and Deployed.
Not Prototyped.

Real systems. Real clients. Numbers you can verify. Here's what the work actually looks like.

How It Works
92% API cost reduction
4x Speed increase on key workflows
<60 sec Replaced a 22-step manual process
97% Token reduction on repeated tasks
3+ Years deploying AI in production
Legal Build

From Hours Per Client to an Agent That Prepares the Paperwork

A lawyer automated client intake document prep, removing a bottleneck that ran through one person.

Hours → Minutes Per client, on intake document preparation
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Legal Map → Build → Enable

From Manual Chasing to a Collections System That Runs Itself

A lawyer replaced manual collections tracking with a system. He now saves dozens of hours a month.

Dozens of hours Saved every month on collections work
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Digital Business Map → Build → Enable

Slash: A Business AI Partner for Lidor Levy

Lidor needed a system to run his entire business solo - content, research, strategy, calendar, and social media. We built Slash.

1 system Runs content pipeline, research, CRM, calendar, and social media
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AI Infrastructure Internal

The Operating System Under My Agent

Why I stopped building custom API agents and built a local Agentic OS instead - and how it cut costs by 92%.

92% API cost reduction vs. custom agent approach
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Education Internal

39 Courses, 572 Lessons, 498 Students - Migrated in 4 Days

Schooler was holding the content back. We rebuilt the entire LMS from scratch - production-ready in 4 days.

4 days Full LMS migration to production
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Content Marketing Internal

Automating a CRM That Had No API

Responder.live had no automation API. We built a Playwright-based browser pipeline that gave us full API-level control anyway.

0 → full Newsletter pipeline automated - no API required
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Similar challenge?

If something in these case studies looks like your situation - let's talk. A 60-minute mapping session is enough to know if there's a fit.