If it isn't documented, it can't be automated.
Most AI projects stall before anything gets built. The process lives in one person's head, the data sits in five systems, and nobody owns the result. I document your workflows, write the SOPs, and clean up the data first. Then I automate what's ready, so your team trusts it and actually uses it.
Twenty years as a COO and operating executive, including helping scale a healthcare network from $0 to $265M.
Live queue
Running“What time does the fuel dock close today?”
Hoursconf 0.97Draft ready“Can I move my reservation to next weekend?”
Reservation changeconf 0.88Staff review“I think I was charged twice last month.”
Billing disputeconf 0.62Escalated
Sample run on test data. Every reply waits for staff approval.
Before AI
AI speeds up whatever you give it. Give it something worth speeding up.
Clear processes get faster with AI. Unclear ones get louder. Every engagement starts by checking five foundations, because these decide whether AI pays off or gets quietly switched off three months later.
Ownership
One named person owns each workflow and its results. Without an owner, nobody fixes the AI when it drifts, and nobody notices that it has.
Documented workflows
Every step, handoff, and exception is written down. AI can only follow a path someone has drawn.
Written SOPs
The way your best person does the job, on paper. This becomes the instruction set the AI works from and the standard it gets checked against.
Clean, connected data
One version of each customer, invoice, and job, in systems that can talk to each other. Duplicate and outdated records turn into wrong answers sent with confidence.
Systems that fit
Your CRM, accounting, and inbox set up the way your team really works. AI layered on a tool nobody uses well becomes one more tool nobody uses.
See how your operation scores
Take the 2-minute readiness checkThe Earned Autonomy method
AI starts supervised. It earns more responsibility with results.
Your team stays in control from day one. AI takes on more work only after it proves it can handle it, one task at a time.
Autonomy level
Red: You decide
- Stage 1 · Red
You decide
AI researches and drafts. A person makes every call. Every new workflow starts here, along with anything involving money, legal risk, or an unhappy customer.
- Stage 2 · Yellow
You approve
AI does the work, and a person reviews it before a customer sees anything. Anything the AI is unsure about goes to a separate queue.
- Stage 3 · Green
AI runs, you spot-check
A task moves here only after it hits an agreed accuracy rate on your real work. A sample is still reviewed every month. If accuracy slips, it goes back to Yellow.
How an install runs
Five phases. Foundations before the build.
Owners, documentation, and clean data are in place before anything gets automated. That's why the automation keeps working after I hand it over.
- 1
Align
Leadership interview, readiness score, a check for AI tools your team already uses on their own, and a list of your top 10 workflows.
- 2
Document
Map the three highest-value workflows, write their SOPs, clean the data they depend on, and record today's time and error rates.
- 3
Govern
Name owners, set risk levels, and decide who approves what and what happens when the AI gets something wrong.
- 4
Deploy
Build on your existing tools. Run alongside your current process until quality targets are met.
- 5
Optimize
Monthly review of results, exceptions, and cost. Tasks move from Yellow to Green as they earn it.
Work with Daniel
Diagnose. Fix the foundations. Build. Oversee.
Every engagement follows the same order. Most owners know within two weeks which workflows to fix first and which are ready to automate.
Every project is a fixed fee, quoted in writing after a 30-minute call. You know the full cost before anything starts.
Step 1
AI Ops Diagnostic
2 weeks · fixed fee
Find the three workflows worth automating first, what has to be fixed before each one, and a 90-day plan.
Step 2
Foundations Sprint
1–3 weeks · fixed fee
Document the workflows, write the SOPs, and clean up the data your first automation depends on. You own every document, whether or not you automate.
Step 3
Governed Build
3–6 weeks per workflow · fixed fee
One workflow built on your tools, tested against your real work, with approval rules and a plan for when it gets something wrong.
Step 4
AI Ops Oversight
Monthly retainer
Monthly accuracy and cost review, fixes after changes, and one new workflow each quarter.
The operator behind the work
AI is new. Running operations isn't.
I've spent twenty years building and running operating companies in healthcare, real estate, and logistics. The same discipline goes into every AI install: clear owners, written processes, measured results, and controls that keep it from breaking.
$0 → $265Min revenue in under 5 years. As SVP of Operations, I built the operating platform for Align Networks through its acquisition.
12X EBITDA exitto General Atlantic. As acting President of Techhealth through my consulting firm, I designed and ran the exit strategy.
$1B real estate portfoliooperated as COO of The Klotz Group. Occupancy rose from 94% to 98%.
$15M → $75Min revenue in under a year at One Call Care Management, from a clinical performance platform I helped build.
Companies where I've built and automated operations
Blueprints
See exactly what gets built before you buy it.
Working reference builds that show the workflow, the tools, the controls, and what each one measures. Each is labeled as a reference build. Client results appear here only with real before-and-after numbers and the client's permission.
Drag to explore →
Hospitality & booking
Inbound Email Triage Agent with Eval Gating
An agent reads every inbound email, classifies it, pulls facts from your systems, and writes a draft reply for staff to approve. Nothing sends automatically until it's measured.
Reads every incoming email and drafts a reply for your staff to approve.
See the architecture →Cross-industry
Ops Knowledge Brain (Cited Answers from Your SOPs)
Staff ask questions in plain English and get answers drawn only from your policies and SOPs — with the source document and page cited under every answer.
Answers staff questions straight from your SOPs and shows which document it used.
See the architecture →Property management & finance
Document Intake Extractor (Invoices, Leases, Contracts)
Uploaded invoices, leases, and contracts are parsed into structured fields with a confidence score on each one, then written to your tracker — low-confidence fields flagged for a person.
Reads invoices, leases, and contracts and enters the details, flagging anything it's unsure about.
See the architecture →Professional services
60-Second Lead Intake Pipeline
A web inquiry is captured, researched, scored, logged in HubSpot, answered with a personalized first reply, and announced to your team in Slack — in under a minute.
Answers new web inquiries in under a minute and logs them in your CRM.
See the architecture →Cross-industry
AI Ops KPI Dashboard with Weekly Narrative
A live dashboard of your core operating numbers, plus a plain-English weekly brief that explains what moved, flags risks, and recommends three actions.
Puts your key numbers on one screen and writes a plain-English summary each week.
See the architecture →Cross-industry
Multi-Agent Operations Advisory Team
Four specialized agents — research, strategy, marketing, and operations — work in sequence on a business challenge and hand back an executive brief with a 30-day plan.
Turns a business question into an executive brief with a 30-day plan.
See the architecture →Professional services
Sales Pipeline and Forecast Board
Every open deal on one board by stage, with live totals and a forecast weighted by stage win rates, so you see what is likely to close without maintaining a spreadsheet.
Shows every open deal and a weighted forecast without a hand-kept spreadsheet.
See the architecture →Professional services
Staged Client Onboarding Checklist
A step-by-step onboarding flow where finishing one stage unlocks the next, every step has one owner, and progress is visible to your team and the client.
Keeps every new client's onboarding on track so steps don't get skipped or asked for twice.
See the architecture →Cross-industry
SOP-Driven Task Drafts
A task board that shows the written procedure next to each task and drafts a first pass from the two, so your team starts from a draft that follows your own process.
Drafts the first pass of each task from your own written procedure.
See the architecture →Keep learning
Straight answers
We already pay for ChatGPT or Copilot. Why do we need this?
Those tools help one person at a time. Your operation changes when AI is connected to your inbox, CRM, and accounting, and follows a process someone owns and measures. That connection and that process are what I install.
Our processes aren't written down. Is that a problem?
That's normal, and it's where most engagements start. The Foundations Sprint documents your workflows and SOPs with your team. You keep everything, even if you decide not to automate.
Will AI send things to our customers without us seeing them?
Only if you decide it should. Every workflow starts with AI drafting and your team approving. A task runs on its own only after it holds an agreed accuracy rate on your real work.
Do we need developers or new servers?
No. Builds run on tools you log into, like Make, n8n, your CRM, and an AI provider. Each one comes with a plain-English runbook your team can follow.
Are the blueprints real client projects?
They're working reference builds, labeled that way. They show exactly what I'd install and how it's controlled. Client results are published only with measured before-and-after numbers and permission.
How do you measure whether it worked?
Before building, I record time per task, cost per unit, and error rate. Every month I report against those numbers, one workflow at a time.
Find out what's ready to automate and what needs fixing first.
A 30-minute operations call. We look at where your team's hours go, what's written down, and whether AI is the right fix yet. If it isn't, I'll tell you what to do first.