Operator-built, finance-grade, human-approved AI automation. Below: systems I built for a North American back-office services firm, then products and systems I have built on my own, from a voice-first assistant to a shipped learning marketplace.
Each build below reads messy real-world inputs with AI, hands the decisions that matter to deterministic code, and stops for a human to approve anything that changes a record. That is what "finance-grade" means here: nothing posts on its own.
Everything below is grouped in two. First, AI automation I built for a North American back-office services firm, anonymized where client confidentiality applies. Then systems and products I have built on my own, where there is no client to protect. Either way, no client is named, sensitive domains stay general, and every diagram and mockup is a schematic with placeholder numbers, never a real screen or real client data.
Systems I built for a North American back-office services firm, anonymized where client confidentiality applies. Every number in every diagram is a placeholder.
For a North American back-office services firm, I built the engine that assembles its entire monthly client invoice book automatically, pricing every line from the signed contract rather than a drifting spreadsheet and flagging anything uncertain for a human.
I then added a self-improving accuracy loop that grades its own output against the approved books and keeps only changes that raise the match, which recovered tens of thousands of dollars a year in quietly under-billed revenue.
One firm's outcome, not a typical or guaranteed result.
For a North American back-office services firm, I built a voice-guided AI agent that runs the firm's monthly client billing close alongside a person. It checks the data is ready, drafts every record, and proposes each system write as a card the operator approves by voice or click, with nothing posted on its own.
It runs inside the firm's own systems and turned a long manual checklist into a three-step guided flow used by non-technical staff.
One firm's outcome, not a typical or guaranteed result.
For a North American back-office services firm, I built a pipeline that moves a client's compliance and personnel documents from a prior provider's system into the new one during onboarding, work that used to be done by hand file by file.
It reads the old system's API, re-labels and sorts every document to the new system's rules, and packages it for a one-shot upload. One migration handled several thousand documents at around 99% success, in hours rather than days.
One firm's outcome, not a typical or guaranteed result.
For a North American back-office services firm, I built an AI layer over its client support desk: it drafts the first reply from an approved knowledge base for a human to send, and it refuses to answer anything sensitive on its own.
I also built a live pipeline that catches every missed-call voicemail, transcribes it in seconds with cloud speech AI, and drops the text straight onto a tracked support ticket so nothing gets lost, for about a dollar a month.
One firm's outcome, not a typical or guaranteed result.
Independent builds of my own, with no client involved, from a voice-first assistant and a walled-off multi-agent system to a shipped learning marketplace and an AI video pipeline.
I built Jarvis, a bilingual English and Spanish voice-first AI assistant with a live heads-up display. I talk to it hands-free and it gives me a morning brief, project status, and upcoming deadlines, captures notes and rules by voice, and shows live widgets, with an AI model as the brain and my own intent router pre-loading the right context for each request.
Anything that writes is read back and confirmed first, so nothing is saved without my okay. Built with Python, faster-whisper for speech-to-text, neural text-to-speech, a browser HUD over WebSocket, and a custom intent router with confirm-before-write.
One operator's own build, not a typical or guaranteed result.
I designed and run a multi-agent AI assistant on a small always-on server, reachable from a phone, that can send email, manage a calendar, and handle files, with a hard rule that only its owner can trigger any action that reaches out or changes something.
It includes a separate front-desk agent for outside contacts whose safety is built into its structure rather than a promise: it has no access to private tools at all, and it can confirm meeting times through a calendar view that shows open slots but never the details.
One operator's own build, not a typical or guaranteed result.
I designed and built Pocketclass, a full online learning marketplace, as an independent product. Instructors publish courses and live sessions, students browse and buy, and an admin oversees the whole thing, with real sign-in, Stripe payments, and a commission-based pricing engine.
It runs on a modern web stack, Next.js, React, Supabase, and Stripe, and ships a demo mode that works entirely on sample data, so it can be shown safely without touching a real database.
One operator's own build, not a typical or guaranteed result.
I built an AI-assisted video editing pipeline that turns raw footage into a finished cut without a manual timeline. It extracts the audio and frames, transcribes them, and asks a model to choose the strongest clips and write an edit list, then renders the final video programmatically in both wide and vertical formats.
It turns a manual editing session into a repeatable, code-driven process a person still reviews. Built with Node.js, Remotion, faster-whisper, and ffmpeg.
One operator's own build, not a typical or guaranteed result.
The system prepares the work and proposes each change. A person reviews and approves anything that matters. Nothing runs on its own.
AI reads and drafts; plain code does the math and the rules, so the same inputs always give the same answer and results are auditable.
Built to run in the tools you already have, like Business Central, Azure, and Microsoft 365, with least-privilege access and a security one-pager for your IT team.
Results described on this page are one operator's or one firm's outcome and are not a typical or guaranteed result. Every visual is a schematic mockup with placeholder numbers, not a real screen or client data.
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