Floor, pit and table games
Dealer background, pit supervision, game pace, ratings, disputes, fills, credits, dealer mistakes, supervisor notes and daily table results.
CasinoOpsAI is my proof of work. It shows how 30+ years across the floor, pit, cage, surveillance, slots, reporting, SOPs and casino systems can be used with modern AI tools to solve problems casinos already know.
This site is not mainly here to sell a box of software. It is here to show that I follow the technology and can use it in a casino way.
A casino may already have a CMS, but managers may still struggle with reports, screenshots, PDFs, Excel files, shift notes, cage variances, slot summaries and department handovers. That is where my experience and the tools shown here fit.
The simple message is this: I understand the casino side, and I can also work with modern AI/reporting tools without giving AI unsafe decision power.
AI and reporting tools are only useful when the person shaping the workflow understands the report, the pressure behind it and the people who have to use it.
Dealer background, pit supervision, game pace, ratings, disputes, fills, credits, dealer mistakes, supervisor notes and daily table results.
Cash control, reconciliation, variances, approvals, handovers, document discipline, cashier-window pressure and written follow-up.
Incident review, camera-based follow-up, dispute support, report writing, weak procedure detection and staff accountability.
Machine performance, jackpot and hand-pay follow-up, floor checks, technical notes, shift comments and management review of slot results.
Opening and closing pressure, staff coordination, guest issues, daily summaries, unresolved items and manager handover discipline.
Casino system rollout experience, Excel-based reporting, KPI review, player-tracking validation and turning system output into usable management information.
Most casino reporting problems are not caused by a lack of data. The data is usually there. The problem is that it is scattered, late, unclear, hard to compare or not reviewed in a consistent way.
That is why CasinoOpsAI focuses on practical workflows: upload, check, review, approve, report and follow up. AI can help inside that structure. It should not replace casino judgment.
They are not decorations. They show how I structure casino problems, set safe AI boundaries and turn daily operational pain into something a manager can actually review.
A practical plan showing how I would listen first, select one useful workflow, and make it repeatable.
View page →A plain HR-readable record of titles, dates, casinos, countries, departments and downloadable full CV.
View page →Practical examples for reporting mess, shift pressure, table mix decisions and promotion value review.
View page →Shows how casino procedures, checklists and training notes can be made clear enough for people and safe enough for AI support.
View page →Shows the casino roles where my operations experience and current AI/reporting capability can fit.
View page →A short employer-facing summary with role fit, first 90 days, proof links and a downloadable PDF.
View page →Explains why sensitive casino reporting should stay under casino control, even when cloud AI is legally possible.
View page →The best fit is a casino that needs experienced operational judgment and better reporting discipline, but also wants someone who can work with current AI tools instead of being afraid of them.
I can support operations, reporting, SOP cleanup, department coordination, management-controlled reporting improvements and the bridge between casino management and IT.
See the employer-focused page →The value is the combination: years of casino reality plus the discipline to keep learning and apply new tools carefully.
Dealing, inspecting and supervising table games built the habits that still matter: accuracy, game pace, control, documentation and quick judgment under pressure.
Cash desk control, surveillance work, internal audit and casino system implementation gave me a wider view of how departments connect and where reports often fail.
Managing shifts and casino departments showed the daily problems GMs recognize: weak handovers, scattered notes, late reports, unclear follow-up and procedures that staff do not use.
CasinoOpsAI is the result: not generic AI hype, but controlled reporting, SOP and workflow examples built from casino operations experience.
The full career includes work in Turkey, Czech Republic, Kazakhstan, Kyrgyzstan, Northern Cyprus and Suriname, with roles covering dealing, pit supervision, cash desk, surveillance, internal audit, system implementation, shift management and casino management.
These extra projects show the same thing from two directions: casino knowledge and AI understanding explained in plain language.
A casino knowledge site built from real operational experience, covering casino procedures, games, player behavior and the practical side of casino work.
Visit chipsandtruths.com →An AI developments site written in plain language. It helps me follow what AI can and cannot do before applying it to casino reporting and workflow problems.
Visit aiupdatewatch.com →I do not believe casinos should hand decisions to AI. I do not believe every problem needs a new system. I do not believe a CMS should be touched carelessly just because a new reporting idea sounds attractive.
The practical approach is smaller and safer: one department, one workflow, one useful report, reviewed by people who know the operation.
If your casino needs experienced operations help with modern reporting, AI-assisted workflows, SOPs or department control, the first conversation can stay simple: what is the problem, where is the pain, and what would a useful first result look like?
Choose the report, handover, dashboard, checklist, or department workflow that causes the most daily friction. Prove value with one controlled improvement before expanding.