01
Revenue systems
Engines that fill the pipeline and keep it warm. Outreach, content, and reporting that run while your team sells.
The one idea to keep
The fix is not smarter AI. It is better architecture around the AI.
We build custom AI systems in your cloud, train your people until they run them, and guarantee the result in writing. You watch it all run in one platform: your AI team, your approvals, your numbers.
Built for owner-led companies where the decision-maker is in the room.
Not there yet? The insights are freeSystems running at
- Hoyes Michalos
- Nurse Next Door
- Fedi
- UBC Sauder
- Merchant House Capital
- Picton Investments
- Campbell Froh May & Rice LLP
- Barnakl
- Hungerford
- Breez
1,500-5,000 hours reclaimed annually
Systems running at
- Hoyes Michalos
- Nurse Next Door
- Fedi
- UBC Sauder
- Merchant House Capital
- Picton Investments
- Campbell Froh May & Rice LLP
- Barnakl
- Hungerford
- Breez
1,500-5,000 hours reclaimed annually
Systems running at
- Hoyes Michalos
- Nurse Next Door
- Fedi
- UBC Sauder
- Merchant House Capital
- Picton Investments
- Campbell Froh May & Rice LLP
- Barnakl
- Hungerford
- Breez
1,500-5,000 hours reclaimed annually
Where we actually are
Everyone uses AI. Almost nobody gets paid for it.
The technology works. The way most companies wire it in does not. That gap is the whole opportunity.
88%
of companies use AI. About 6% attribute more than 5% of profit to it
Adoption is common. Getting paid for it is rare.
McKinsey, State of AI, 2025
95%
of enterprise AI pilots show no measurable P&L return
The tools get bought. The work does not change.
MIT NANDA, The GenAI Divide, 2025
59%
is what 90% per-step accuracy compounds to across five chained steps
A business process has to clear 99% end to end. That is why pilots demo well and die in production.
Arithmetic, not a benchmark
The winners are not decided by who bought the software. They are decided by who rewired the work.
So the fix is architecture
Written playbooks your team can read. Model judgment spent only at the decision points. Deterministic code for everything else, and a human gate in front of anything you cannot undo. That is what we build, and it is why our systems survive contact with a real Tuesday.
Why owners call us
You don’t need an AI strategy. You need Tuesday to run itself.
What done means
Done means the system runs without us, your team actually uses it, and the ROI shows up in your P&L. That is the part we guarantee.
Get a free audit- 01
Your team drowns in manual work.
Follow-ups, reports, data entry, document chasing. Hours every week on work a system should do.
Daily AI use still reaches only about 10% of the US workforce (OpenAI, 2025).
- 02
You bought the tools. Nobody uses them.
Licenses paid, habits unchanged. Adoption is a training problem, not a software problem.
Only 35.8% of workers with paid Copilot seats actually use them (Recon Analytics, 2026).
- 03
The last consultant left a deck, not a system.
Strategy delivered. Nothing shipped. You're still doing everything by hand.
More than 80% of AI projects fail, double the rate of other IT projects (RAND, 2024).
- 04
Leads go cold before anyone follows up.
Your pipeline leaks at the exact moment speed matters most.
89% of midsize businesses plan to put AI to work in 2026 (JPMorgan Chase, 2026).
- 05
Reporting eats your best people.
Investor updates, client briefs, board packs. Senior hours spent formatting, not thinking.
AI-using accountants close monthly statements 7.5 days faster (Stanford GSB, 2025).
- 06
You know AI matters. You don't know where it pays.
Every vendor says everything is possible. Nobody shows you the math.
74% of SMBs stuck at the edge of adoption say clearer ROI evidence would move them (PayPal, 2025).
- 07
Your client list is one paste away from a public chatbot.
Staff use free AI tools on real company data because the sanctioned option doesn't exist. Banning it just drives it underground.
18% of employees paste data into GenAI tools; over half of those pastes include corporate information (LayerX, 2025).
- 08
Your whole AI setup lives in one person's head.
One employee built the automations and knows the prompts and the keys. If they leave, you're back to zero. They know it too.
50% of organizations lack the skilled talent to manage AI (Kyndryl, 2025).
We build the systems. We teach your team. We govern the risk. And we stay until it pays.
Every system ships in the same shape as a company that runs well: a written playbook your team can read and edit, a coordinator that decides who does what, and narrow tools that each do one job every time. Judgment sits in the middle. Certainty sits at the edges.
02
Operations systems
The back office on autopilot. Onboarding, task flow, and live numbers without the Friday scramble.
03
Adoption & change
Systems only pay when people use them. Training, roadmaps, and a guarantee that your team actually adopts.
04
Governance & security
Your data stays yours, and every action leaves a trail. Private AI in your cloud, approval gates, and rules your lawyer can read.
Not a slide deck. A login.
Every engagement runs on the Advizr client platform: your AI team, its work, its approvals, and its numbers, on one page your whole company can see.
The shape is one operator and a handful of AI coworkers, each running one workflow. Judgment stays human. Volume goes digital. The constraint stops being software and starts being leadership, which is good news if you can already run a company.
Approval queue
The AI proposes. You approve. Nothing sends itself.
Cost per outcome
Every dollar of spend traced to the work behind it.
Your AI team
A roster, not a black box.
How autonomy gets earned
- 01Drafts for your reviewIt prepares the work. A person decides whether any of it ships. Zero risk by construction.
- 02Acts with your approvalEach consequential action arrives as a proposal stating its risk, its reversibility, and when it expires.
- 03Acts alone, inside hard limitsRoutine work runs unattended within caps on spend, volume, and who it may contact.
- 04Runs on its own, fully loggedReserved for work that has already proven itself. Every action stays on the record.
Everything starts read only. You grant each permission one at a time, on evidence, the same way you would promote someone new. Caps on spend and volume are hard limits, so a system that runs away stops itself before it costs you anything. Every action it takes lands in a log you can read.
Not a basic AI consulting firm.
Retrieval, agents, evals, fine-tuning, classic ML: ten capability clusters, every tool named, every limit stated. A sample of the matrix:
Answers from your documents, with citations
Hybrid retrieval plus reranking, faithfulness measured before generation
PE deal desk caseA back-office workflow that runs itself
Deterministic execution, model judgment at decision points only, human gate before send
Breez outbound engineProving it works before it ships
Golden datasets and regression gates wired into CI, deploys blocked below threshold
The method, written up90% per-step accuracy compounds to 59% over five chained steps. So we push complexity into deterministic code, and spend model judgment only where judgment is the job.
Compound success rate of a five-step workflow at 90 percent per-step accuracy. Arithmetic, not a benchmark.
The last decade of automation was brittle chains. One upstream change and everything stopped, then sat there until a human noticed. What we build reads its own error, repairs the instruction that caused it, and runs again.
A failure leaves the system better than it found it.
Proof
We guarantee 5x ROI inside 30 days of deployment, in writing, measured against a baseline you sign before we build. If the system misses the bar, we keep working for free until it clears.
5x ROI in 30 days. Or we work for free.
In writing, on every first build. Read the full terms
25-50%
of manual hours reclaimed
Basis: measured engagements, before/after baselines on targeted workflows.
Lead generation · Breez
Minutes per researched prospect instead of hours
An outbound engine that researches, qualifies, and follows up around the clock, built and run for Breez. 40+ personalized data points per proposal.
How it works
Weeks, not quarters.
01Week 0
Discovery
We map your operations and pick the highest-ROI build.
02Weeks 1-3
Optimization
The workflow is redesigned and prototyped on your real data. You see it working before full deploy.
03Weeks 4-8
Implementation
Live in your tools, in your cloud. Your team trained, not just handed a login.
04Week 9+
Post-deployment
Monitoring, fixes, quarterly roadmaps. The guarantee is measured here. Cancel anytime.
Real clients. Running systems.
All case studiesLead generation · Breez
Minutes per researched prospect instead of hours
Minutesper researched prospect, was hours
Read the case studyInterior design · An interior design firm
One firm, five systems: rebuilding operations end to end
5systems in one build
Read the case studyPrivate equity · A $13B private equity fund
Deal intel prep cut from 6 hours to 20 minutes
6h → 20mdeal intel prep
Read the case studyProfessional services · A national professional services firm
AI adoption from 23% to 85% across a national firm
23% → 85%active AI adoption
Read the case study17 industries. One method.
The same build method everywhere; the workflows, compliance rules and proof change per industry. Where we have a published case study, the tile says so. Where we do not, it says that too.
The technology was the easy part.
Budgets go to software. Pilots go nowhere. The gap is people, and closing it is half of every engagement: training built on your real workflows, until the hours come back.
93/7
Companies spend 93% of AI budgets on technology and only 7% on the people expected to use it (Deloitte, 2025).
48%
48% of employees rank training as the most important factor for AI adoption, yet nearly half receive minimal or none (McKinsey, 2025).
23% → 85%
active AI adoption at a national professional services firm, after role-specific training. The case study
- 01Literate
- 02Applied
- 03Operational
- 04Transformational
- · the maturity ladder, tier by tier on /education
Where we fit. And where we don't.
The real alternatives, compared honestly. Where another column is the right answer, this table says so.
Time to a live system
Advizr
Weeks. First build runs in production, not in a deck.
Hire in-house
Months. Hiring alone takes one or two.
Big-firm consulting
Quarters. Discovery comes first, then the build team.
DIY with ChatGPT
Today for one person. Rarely for the whole team.
Lowest upfront cost
Advizr
Fixed scope after a free audit. Not the cheapest first dollar.
Hire in-house
The most expensive option on the table.
Big-firm consulting
Significant fees before anything ships.
DIY with ChatGPT
A subscription and an afternoon. This one's yours.
You need a full-time data science team
Advizr
Not us. We build and train; we don't staff your lab.
Hire in-house
Hire. This is exactly what an internal team is for.
Big-firm consulting
They'll assemble one for you, slowly, on their paper.
DIY with ChatGPT
Not a chance.
You need board-level strategy only
Advizr
Not us. We only take work that ends in a running system.
Hire in-house
Wrong tool. Builders build.
Big-firm consulting
Their home turf: brand-name cover for big decisions.
DIY with ChatGPT
You'll get a strategy that sounds like everyone else's.
Deep knowledge of your business
Advizr
We learn fast and baseline everything, but your people live it.
Hire in-house
Unbeatable. They're inside it every day.
Big-firm consulting
A new analyst learns your business on your invoice.
DIY with ChatGPT
You have it. The chatbot doesn't.
A team trained to run it without the builder
Advizr
Education is half of every engagement, not an add-on.
Hire in-house
Depends entirely on whether that person documents and teaches.
Big-firm consulting
Training is usually a separate line item.
DIY with ChatGPT
You are the training program.
Accountability for the result
Advizr
5x ROI in 30 days, in writing, measured against a signed baseline.
Hire in-house
Performance reviews. No contract covers the outcome.
Big-firm consulting
Deliverables are guaranteed. Outcomes rarely are.
DIY with ChatGPT
You're accountable to yourself.
Still running a year later
Advizr
Documented systems, runbooks, two trained people per process.
Hire in-house
Strong while the builder stays. Key-person risk after.
Big-firm consulting
Depends on who renews the contract.
DIY with ChatGPT
Dies with the spreadsheet that held it together.
Bold marks the honest best answer per row. It is not always us.
Run your own numbers.
Three sliders. Conservative bounds. The same math we put in front of clients.
Your operations
4
10
$45
Savings use the low end of our 25-50% hours-reclaimed range. The math is conservative on purpose.
The math
Cost of manual work / yr$86,400
Recovered / yr$21,600 - $43,200
Hours back / yr480+
Hours back / wk10+
Calculated at the low end of every range.
Every first build is covered in writing: 5x ROI in 30 days. Or we work for free.
Three ways in
Three ways in. No long discovery.
Free · 3-5 days · No obligation
AI Opportunity Audit
We map your operations, find the highest-ROI automations, and hand you a ranked plan with payback math. Yours to keep, whoever builds it.
No obligation. No follow-up sequence.
Paid · Fixed scope
First Build
One high-ROI system, built on your real data and deployed in your stack, with your team trained to run it. Fixed scope. Quoted after the audit. Covered by the 5x ROI guarantee.
Scope my first build
Paid up front. Cancel anytime after.
Free · 15 min
Intro Call
Fifteen minutes with James, not a sales rep. Bring your worst bottleneck, leave with a straight answer.
Book 15 minutes
No pitch deck.
Every first build is covered: 5x ROI in 30 days. Or we work for free. Read the full terms
Questions? Straight answers.
Start free. Know your number in five days.
A 3 to 5 day audit of your operations, ending in a plan with the ROI math attached. No obligation.