Creative Ops & Production | Ads, Explainers & Motion
Data Analyst | Power BI & SQL Dashboard Expert
Data Analyst | Power BI & SQL Dashboard Expert
Senior Accountant for US, UK & Australian Businesses
I performed a full QuickBooks Online cleanup, reconciliation, and month-end close for a US-based service business.
The work included reviewing and correcting transaction classifications, cleaning up uncategorized and duplicate entries, reconciling bank and credit-card accounts, reviewing accounts receivable and payable, updating the chart of accounts, and recording accruals, prepayments, depreciation, and other adjusting journal entries.
I also prepared monthly Profit and Loss, Balance Sheet, cash-flow, AR aging, AP aging, and budget-versus-actual reports. The completed cleanup gave management more reliable financial information, clearer visibility into cash flow and profitability, and a structured process for future monthly reporting.
Sample data has been used to maintain confidentiality.
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I managed the complete monthly bookkeeping and month-end close process in Xero for Australian service-based businesses.
The work included bank and credit-card reconciliations, accounts receivable and payable reviews, payroll and superannuation checks, accruals and prepayments, GST/BAS review, intercompany reconciliations, and investigation of outstanding or incorrectly classified transactions.
I also prepared Profit and Loss, Balance Sheet, cash-flow reports, and management summaries to give clients clear visibility into profitability, cash position, receivables, payables, and compliance obligations. A structured close checklist was used to ensure accurate records, timely reporting, and completion of all month-end activities.
Sample data has been used to maintain confidentiality.
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I managed a structured month-end close and balance-sheet reconciliation process to ensure the financial records were complete, accurate, and ready for management reporting.
The work included bank and credit-card reconciliations, accounts receivable and payable reviews, accrual and prepayment entries, fixed-asset and depreciation updates, deferred-revenue analysis, intercompany reconciliations, and investigation of unusual general-ledger balances. These activities align with my experience directing close processes and maintaining supporting accounting schedules.
I also maintained a reconciliation tracker to monitor account balances, supporting documents, unresolved differences, review status, ownership, and completion deadlines. This helped identify outstanding issues, strengthen financial controls, and support accurate and timely reporting.
Sample data has been used to maintain confidentiality.
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I prepared a detailed budget-versus-actual analysis to help management evaluate monthly and year-to-date financial performance against plan.
The report compares actual results with budget across revenue, cost of sales, gross profit, operating expenses, EBITDA, and net profit. It calculates both value and percentage variances, identifies favorable and unfavorable movements, and explains the main drivers behind each significant variance.
I also included trend analysis, visual summaries, management commentary, and recommended actions to support cost control, profitability improvement, and better financial decision-making. The model was designed as a reusable monthly reporting tool that can be updated after each accounting period is closed.
Sample data has been used to maintain confidentiality.
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AI Agents & Automation That Ship | Voice AI, LLMs, .NET
Not every automation needs an LLM. A B2B business was receiving purchase orders from retailers through SPS Commerce.
Someone on the team then checked stock by hand, retyped each order into Sage 50 as a sales order, and kicked off fulfillment.
We built a platform on ASP.NET (http://ASP.NET) Core and C# that handles it end to end.
→ Purchase orders come in through SPS Commerce automatically
→ Stock and order details are validated before anything is created
→ Sales orders land in Sage 50 without retyping
→ Anything odd is flagged as an exception instead of quietly slipping through Less manual work across the whole order lifecycle, cleaner data, and orders that keep moving without anyone copying fields between screens.
I build a lot of AI agents now. But the first question on every scoping call is still the same.
Does this need judgement, or does it just need wiring? Half the time it's wiring.
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An AI agent that can move money is only useful if it can't move it on its own.
Haia lets people manage their banks, crypto wallets and brokerages from one place, just by saying what they want done.
I built the AI orchestration layer. The interesting design choice wasn't the model. It was deciding where the model is not allowed to act.
→ The agent turns a request into a step by step plan
→ Each step is routed to the right banking API, wallet, chain or brokerage
→ Security and compliance checks run before anything executes
→ The user approves every move, and transactions are signed on their own device It's fully non-custodial.
Zero private keys ever exposed, to us or to the model.
If you're building agents that touch money, health records or anything you can't undo, design the approval step first. Then the AI.
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Your support team goes home at 6. Your customers don't. An online retailer we worked with had a simple problem.
Shoppers asked questions at 11pm, on weekends, on holidays.
The answers came the next morning, and by then a lot of them had bought somewhere else.
We built a 24/7 AI chat into their store. Not a FAQ bot that says "I didn't understand that" three times.
It handles the common questions on its own, recommends products inside the conversation, and drops every chat into their CRM with the full customer context.
When a person does step in, they aren't starting from zero. → 50% more conversions → 35% faster response time → 40% less support workload The part people underestimate is the CRM link.
A chat that sells but forgets who the customer is just moves the problem to your team. Support and sales turned out to be the same conversation.
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Data Visualization & Operations Expert 📊
Data Visualization & Operations Expert 📊
Digital Marketer/Performance Marketer/Social Media Manger
Digital Marketer/Performance Marketer/Social Media Manger
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Data Analyst | Power BI Developer | Excel, SQL, Python
Data Analyst | Power BI Developer | Excel, SQL, Python
Beyond the 5 Stars: The Unseen Story in Amazon Alexa Reviews
Everyone talks about how much people love their Alexa devices. The overall ratings are stellar (4.46/5 avg.), and yes, a dominant 76.35% of reviews are positive. But the real story is often in the shadows of that success.
Despite 76% positive sentiment, 1 in 5 reviews are neutral/negative. Even 1-star reviews contain words like "Love," showing frustration from disappointed fans.
The word cloud tells the real story: alongside "Great" and "Easy" sit "problems," "doesn't," and "disappointed."
Mass appeal can mask specific, recurring failures. True excellence solves for the criticism hidden in the praise.
#AmazonAlexa #VoiceTech #CustomerFeedback #SentimentAnalysis #DataAnalytics #ProductManagement #Tech #UserExperience #DataStorytelling #PowerBI
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Strategist by training, builder by instinct
