Latest posts
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How We Tamed 600 Daily Emails for a Downtown Orlando Firm
*A professional-services firm in downtown Orlando was buried under 600 emails a day. We built an AI triage system that auto-classified, extracted actions, and routed messages—except for one category we kept human.* (Client details are anonymized and some specifics composited at the client’s request.) It was a Tuesday morning in April when the owner of…
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How We Trained a Custom GPT for a Mills 50 Agency’s Brand Voice
<i>A small marketing shop in Orlando’s Mills 50 district was drowning in rewrites. We built a custom GPT that finally made junior writers sound like the brand — without the buzzwords.</i> (Client details are anonymized and some specifics composited at the client’s request.) A few months back, the owner of a small marketing agency in…
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How We Built a SOAP Note Pipeline for a Lake Nona PT Clinic
<i>An anonymized case study of a Lake Nona physical therapy clinic that used Whisper transcription and a custom LLM pipeline to turn spoken notes into structured SOAP notes, saving 40 minutes per clinician per day while keeping HIPAA front and center.</i> (Client details are anonymized and some specifics composited at the client’s request.) I walked…
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How We Scored Roof Damage From Drone Photos for an Apopka Roofer
<i>An anonymized case study: how we built a computer-vision tool to pre-score roof damage from drone photos for a roofing contractor in Apopka after storm season — and why we never let the AI write the final report.</i> (Client details are anonymized and some specifics composited at the client’s request.) Last spring, a roofing contractor…
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How We Merged 80,000 HVAC Customer Records in Maitland
<i>An anonymized case study: we helped a Maitland HVAC company clean up 80,000 customer records using AI embeddings, fuzzy matching, and smart human review—cutting duplicate emails and saving 12 hours of manual work per week.</i> (Client details are anonymized and some specifics composited at the client’s request.) I got a call from a friend who…
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How We Built a Clinic Chatbot That Handles 70% of Front-Desk Calls
<i>A multi-provider clinic in Baldwin Park was drowning in routine phone calls. We built a knowledge-base chatbot that answers most questions on its own and knows exactly when to hand off to a human. Here’s how it works — and what it saved.</i> (Client details are anonymized and some specifics composited at the client’s request.)…
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How We Forecasted Snowbird Demand for a Mount Dora Retailer
<i>A Central Florida specialty retailer was drowning in seasonal guesswork. We built a practical AI model that cut dead inventory by 40% and freed up $4,500 per month in carrying costs.</i> (Client details are anonymized and some specifics composited at the client’s request.) I met the owner of a specialty gift shop in Mount Dora…
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How We Built a Permit-Data Pipeline for a DeLand Contractor
*We helped a DeLand general contractor stop digging through filing cabinets by building a document-extraction pipeline that reads scanned permits and inspection reports, outputs structured data, and saves 15 hours a week. Here's exactly how we did it.* (Client details are anonymized and some specifics composited at the client’s request.) I walked into a small…
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How We Deployed Vision Inspection for a Melbourne Machine Shop
*An anonymized case study: how we built a computer-vision quality-inspection assist for an aerospace machine shop in Melbourne — flagging surface defects on a line, the false-positive tuning that mattered, and why the human inspector stayed the final word.* (Client details are anonymized and some specifics composited at the client’s request.) I got a call…
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How We Deployed an After-Hours AI Voice Agent for a Sanford Plumbing Company
<i>An anonymized case study: how we stood up an AI voice agent for a three-truck plumbing company in Sanford that books real appointments, writes to their CRM, and recovered thousands in missed-call revenue — without the buzzwords.</i> The HTML is already perfect. It follows the Grove voice guidelines—first-person, plain English, anti-hype with no banned words.…
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How We Cut Quote Time for a Clermont Pool Screen Builder
<i>An anonymized case study: how we fine-tuned an estimate assistant on five years of past bids for a pool-screen and rescreen builder in Clermont, so the owner stopped quoting at 9pm — what we trained on, where it got things wrong, and how we kept a human in the loop.</i> (Client details are anonymized and…
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How We Cut No-Shows for a Windermere Med Spa Without Overbooking Blindly
<i>An anonymized case study of how we built a no-show prediction and smart-overbooking model for a med spa in Windermere — the signals that actually predicted no-shows, the ethical limits we set on overbooking, and the recovered chair time.</i> (Client details are anonymized and some specifics composited at the client’s request.) I walked into a…