We help businesses put AI to work.

Challenge. The board wanted "an AI initiative" and had a $40K customer-chatbot proposal on the table.

What happened. The assessment showed the chatbot solved a problem customers did not have, and found the real bleed in dispatch: four dispatchers retyping bills of lading and proof-of-delivery data into the TMS. Recommendation: kill the chatbot, spend a fraction of the budget on document extraction into the TMS.

Outcomes. $40K misspend avoided outright. Document automation reclaimed 22 dispatcher hours per week and cut billing-document errors 31%, which shortened the invoice-to-payment cycle by 4 days on average. ROI math: 22 hrs x $52 x 4.3 = $4,919 per month gross, minus $210 in tooling, roughly $4,700 monthly net.

Challenge. Front-desk teams were drowning in insurance verification and recall scheduling, but any AI touching patient data raised compliance alarms.

What happened. The assessment drew the compliance line first: what AI may touch, what it must never touch. Every recommendation lived on the safe side: insurance-verification summaries from payer portals, and an automated recall-scheduling sequence with human confirmation on every patient contact.

Outcomes. 9 front-desk hours reclaimed per week across locations, and recall rebooking up 18% because follow-ups stopped slipping. ROI math: 9 hrs x $38 x 4.3 = $1,472 per month, plus roughly $3,900 per month in recovered recall revenue at their average visit value. Zero compliance exceptions in the first quarter.

Challenge. Monthly client reporting meant assembling 40 reports by hand from three systems. Two partner-days plus staff time, every single month.

What happened. A scheduled Claude skill now pulls from the same three sources, drafts every report to the firm's template, and queues them for review. Humans approve and send; nothing goes out unread.

Outcomes. Assembly went from roughly 32 hours a month to a 6-hour review pass. ROI math: 26 hrs x $120 x 1 cycle = $3,120 per month net of negligible tooling. Build fee recovered inside the second monthly cycle. Partner comment for the record: month-end stopped being a dreaded week.

Challenge. Every signed contract kicked off the same manual ritual: duplicate the project template, generate documents, set reminders, chase the deposit. Thirty-five minutes per booking, and steps got missed in peak season.

What happened. One automation now fires on contract signature: project created, documents generated and sent, reminder cadence set, deposit follow-up scheduled. A second small skill drafts the week-of coordination email from the event record.

Outcomes. 35 minutes per booking to under 3, across 22 bookings a month, roughly 12 hours back monthly for the coordinator. Late deposits down 40% because follow-up never slips. ROI math: 12 hrs x $45 x 1 = $540 per month in time alone, before the cash-flow effect of on-time deposits. Build paid for itself in the first quarter.

Challenge. Every new listing triggered 400 to 500 emails from prospective buyers, most asking the same five questions. The principal was the bottleneck on every deal.

What happened. For each listing, a knowledge assistant trained on the seller-verified marketing package. Buyers get a link instead of an email address; the assistant answers around the clock, with sources, and flags genuinely novel questions to the principal.

Outcomes. Routine buyer email to near zero, roughly 30 principal-hours saved per listing across 7 listings a year, about 210 hours annually. Buyers get answers in minutes instead of days, and both sides of the last deal called the process the smoothest they had seen. ROI math: 210 hrs x $250 principal rate = $52K per year in recovered selling time.

The proof that ships with the offer: this site, its design system, the Progressive Disclosure pattern library, and the automated publishing pipeline behind it (drafts, verifies citations, renders visuals, stages a pull request, pings a phone for one-tap approval) were designed and shipped with the same AI-accelerated build process offered here. Weeks, not quarters.

Challenge. A credible firm with a site that converted nobody: no clear offer, a contact form that went to a shared inbox, inquiries evaporating.

What happened. Repositioned and rebuilt in three weeks: clear practice-area offers, proof up front, one action everywhere (book a consult). An intake assistant now triages inquiries by matter type and urgency before they reach an attorney.

Outcomes. Booked consultations up 2.4x in the first sixty days, and attorneys stopped triaging junk inquiries, roughly 4 hours a week returned to billable work. Build cost roughly a quarter of the agency proposal it replaced. ROI math: 4 hrs x $310 billable x 4.3 = $5,332 per month in recovered billable capacity, before counting the new consults.

Challenge. Every Monday, an analyst rebuilt the same status spreadsheet from system exports, and the ops meeting spent its first twenty minutes arguing about whose numbers were right.

What happened. A small internal dashboard reads the same sources live, and an AI summary on top explains what changed since last week and why it matters. The Monday spreadsheet ritual is gone.

Outcomes. 6 analyst hours a week returned, meetings start at the decision instead of the reconciliation, and stockout incidents fell 12% in the first quarter because exceptions surface daily instead of weekly. ROI math: 6 hrs x $58 x 4.3 = $1,496 per month in analyst time, before the margin effect of fewer stockouts.

The workshop offered here descends from AI adoption sessions designed and run inside a Fortune 100 enterprise: same methods, adapted to your size, your tools, and your workflows.

Challenge. The team wanted AI training that was not a slide deck.

What happened. A half-day working session. The room picked its worst workflow, the client-intake email chain, and built the first automation live before lunch: intake parsed, filed, acknowledged, and routed. The afternoon turned the method into a playbook the team owns.

Outcomes. They left with the automation running, then self-built three more within sixty days using the playbook, no further billable time required. ROI math: the four automations together reclaim roughly 11 team hours a week; 11 x $95 x 4.3 = $4,494 per month against a one-day workshop fee.

Challenge. Plenty of AI ambition, no senior hand to keep it moving month over month.

What happened. The monthly advisory loop: two working sessions a month, one workflow at a time, audit it, cut the fat, automate it. First quarter shipped a tenant-communications skill, a maintenance-request triage automation, and an owner-report generator. A running log keeps every shipped improvement and its savings in view.

Outcomes. 19 cumulative staff hours a week reclaimed by the end of the quarter. ROI math: 19 x $42 x 4.3 = $3,431 per month against the retainer, roughly a 2x monthly return and compounding, since each month adds another automation to the stack.