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HekimaIQ

AI adoption for small & mid-sized business

Start with one thing. Prove it. Then scale.

Big companies get AI working by starting small and measuring hard. Same discipline, your size. I find the one place AI pays off in your business, build it, and show you the number it moved.

A 30-minute call to see if there's a fit.

~95%

of enterprise GenAI pilots deliver little or no measurable business impact.

MIT NANDA · GenAI Divide · Aug 2025
~39%

of organizations can tie any profit impact to AI.

McKinsey · State of AI · Nov 2025

What you get

What AI can actually do for a business your size

AI can take real work off your team: answering customer questions, drafting and routing email, handling scheduling and reminders, pulling answers out of your own documents, flagging what needs attention in your data. The upside is real. So are the risks.

Hekima IQ helps you find the few uses that pay off, set them up safely, and drop the ones that don't.

A sense of what's possible

Systems I've shipped

Real work I've built. Client names kept private.

  • Answers from your own documents. An AI assistant in Microsoft Copilot Studio that turned about an hour of manual lookup per question into seconds.
  • Reminders that run themselves. An automated system sending around 1,000 emails a week across custom schedules, with no added IT workload.
  • Higher collection of payments. Workflow optimizations that raised the collection by 75% in only one week.
  • Know who's about to churn. A prediction model that sharpened retention targeting.

How it works

Aim → Prove → Align → Scale

A ladder, not a leap. Find the win worth building, build it and measure it, then hand it to your team and do it again everywhere else.

  • Step 1 · Aim Find the win worth building
  • Step 2 · Prove Build it, measure it
  • Step 3 · Align Hand it to your team
  • Step 4 · Scale Do it again, everywhere else
  • Ongoing · Advise Keep it working

See what each step includes →


Why trust this

Built by someone who ships it.

Built by a solutions architect who has shipped AI on the Microsoft stack. Every claim here is backed by data you can check, not vendor marketing.

Start with one thing.

Almost everyone should start with Aim: finding the one place AI will pay off, and agreeing what "worked" means before anything gets built.

Book an intro call