the question every business owner asks before signing off on an AI build: "will this actually pay for itself?"

here are the numbers. no theory. no projections. real results from real companies.

commercial real estate: 25% revenue increase in 4 months

the problem: a commercial real estate team was spending 18+ hours per week on outreach. manually researching properties, finding contact info, writing personalized emails, and following up. their pipeline was limited by how many hours they had, not how many opportunities existed.

the build: we automated the entire outreach pipeline. AI researched target properties, enriched contact data, wrote personalized outreach emails based on property type and owner profile, and managed the follow up sequence automatically.

the result: 18 hours per week freed up. the team redirected that time to closing deals instead of finding them. revenue jumped 25% in four months. not from more leads. from actually working the leads that were already there.

build cost: under $15K. the system paid for itself in month one.

ecommerce: 30+ ad scripts per week with zero manual work

the problem: a DTC brand was spending $50K+/month on paid ads. their bottleneck wasn't budget. it was creative. their team could produce 5-6 ad scripts per week. testing at scale requires 30+. they were leaving performance on the table because they couldn't iterate fast enough.

the build: an AI system that analyzed their top-performing ads, identified the patterns in hooks, body copy, and CTAs, and generated new variations in their brand voice. every script was formatted and ready for their media buyer to test.

the result: went from 6 hours of manual copywriting to fully automated. 30+ tested scripts per week. their creative testing velocity went up 5x. cost per acquisition dropped because they were iterating faster than competitors.

build cost: under $10K. ROI in week 2.

construction: lead response from hours to 2 minutes

the problem: a commercial construction firm was losing 30-40% of inbound leads. not because the leads were bad. because their estimating team was buried in active projects and took 4-6 hours to respond. by then, the lead had moved on.

the build: an AI system that captured leads from their website, google ads, and referral forms. it qualified each lead by project type, budget range, and timeline. sent a personalized response within 90 seconds with relevant case studies. booked discovery calls directly on the estimator's calendar.

the result: response time went from 4+ hours to under 2 minutes. qualified meetings doubled in month one. the estimating team stopped wasting time on unqualified leads. no new hires needed.

build cost: $6K. shipped in 3 weeks.

insurance agency: $6K/month in savings from one system

the problem: an insurance agency was managing renewals, client communication, and policy tracking across 3 different tools with manual data entry between them. one operations person spent 15+ hours per week on admin that didn't require human judgment.

the build: a custom CRM and workflow system that unified their tools, automated renewal notifications, tracked policy status changes, and generated client reports automatically.

the result: $6K per month in operational savings. the ops person got 15 hours back per week. error rates dropped because the system eliminated manual data entry. the agency is now white-labeling the system for their PE exit.

build cost: under $20K. the system is now a revenue-generating asset.

the pattern

every client had the same starting point: they knew something was slow or broken. they didn't know what to build. they didn't have a technical team to build it.

that's the whole point of the first call. you tell us where the time goes. we tell you what to build, what it costs, and how fast we ship.

the average ROI across our client base is 4x. meaning for every dollar spent on the build, clients get $4 back in saved time, increased revenue, or reduced headcount needs.