I build production AI that replaces manual workflows—turning spreadsheets, PDFs, and analyst queues into fast, reliable, automated systems.
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Multi-day → same day
AI pipeline that scrubs, validates, and generates investor-facing portfolio reports—delivered same day.
Analysts at Tribute Capital spent multiple days per acquisition manually scrubbing portfolio data—validating claims, cross-referencing records, and compiling investor-facing reports in spreadsheets.
An AI pipeline that ingests raw portfolio data, validates against source records, flags anomalies, and generates formatted investor-ready reports automatically.
What previously took analysts multiple days now completes same-day. Freed the team to focus on deal evaluation instead of data wrangling.
Manual review eliminated
AI reconciles court media against acquired data tapes, flagging discrepancies automatically.
After acquiring distressed debt portfolios, Tribute's team had to manually reconcile court media—proofs of claim, court orders, and plans—against their acquired data tapes, line by line.
An AI system that ingests court documents and data tapes, extracts structured fields, and automatically flags discrepancies—routing only genuine edge cases to human reviewers.
Eliminated line-by-line manual review entirely. Discrepancies that took days to surface are now flagged automatically.
Fully automated matching
Matches deposits against debtor payment histories from trustee records and the NDC API.
Reconciling incoming bank deposits against debtor payment histories required analysts to manually cross-reference spreadsheets against trustee disbursement records and NDC API data.
An automated pipeline that pulls deposit data, matches against debtor payment histories from trustee records and the NDC API, and surfaces unmatched items for review.
Replaced spreadsheet-based reconciliation entirely. Analysts now review exceptions only—not every transaction.
Products
Speak a plain-language instruction and QVA writes it straight into your Quickbase—validated, confirmed by you, and fully audited. Engineered so no freeform AI output ever touches your system of record.
QVA is built so the AI never writes freely to your data. A spoken instruction becomes a structured change scoped to your own Quickbase table, and every write is shown back to you as a plain before/after diff—nothing is saved until you confirm it.
Credentials are encrypted, each organization's data is strictly isolated, and built-in defenses reject attempts to manipulate the assistant. Every change is logged with full before/after history, so you always have a complete audit trail of who changed what.
Field and office teams update records by speaking naturally instead of typing into forms—cutting data-entry time and errors while keeping a human in control and a clean record of every change.
A Chapter 13 claim-valuation engine for institutional debt buyers. PVL prices whole portfolios straight from trustee disbursement data—survival-curve modeling, Monte Carlo risk bands, and investor-ready PDF memos where every number traces back to the model.
PVL prices entire portfolios of Chapter 13 claims directly from trustee payment data, combining survival-curve modeling with simulation to produce a defensible range of outcomes—not a single guess—delivered as investor-ready PDF memos with per-claim pricing and bid guidance.
The engine runs as a self-maintaining system: a coordinated set of AI agents continuously reviews, tests, and validates it. Every number in the client memo is traced back to model output, so each valuation is auditable and defensible.
PVL turns days of manual, spreadsheet-based valuation into fast, consistent pricing—giving debt buyers a repeatable, well-grounded basis for deciding what a portfolio is worth and what to bid.
About Me
I build AI systems for businesses where critical work still happens in spreadsheets, inboxes, PDFs, manual review queues, and analysts clicking through web portals one record at a time.
My career has lived at the intersection of entrepreneurship, fintech, and regulated operations—as a founder, product lead, and hands-on builder. The problems I get pulled into usually arrive as a mess: undocumented processes, conflicting data, tribal knowledge no one has written down, regulations that nobody on the team has fully mapped. The work is sorting through that ambiguity, finding the workflow that's actually running underneath, and rebuilding it into something faster, safer, and more scalable.
I co-founded Abe.ai, an AI virtual assistant platform for banks that was acquired by Envestnet | Yodlee. Selling AI into banks in the mid-2010s taught me what most AI products still get wrong: the hard part isn't the model, it's earning trust from compliance, risk, and the frontline users who decide whether the thing actually gets used.
My current work centers on Claude Code, agentic development, and multi-agent orchestration. I treat AI as infrastructure for production systems—combining domain-loaded context, specialized agents, structured workflows, testing, and human-in-the-loop controls—not just as a coding assistant.
In practice, that's looked like AI systems for complex operating workflows: bankruptcy portfolio valuation, reconciliation between internal systems of record and bank accounts, loan-document triage, and voice-driven structured data entry. One system sorts loan documents against a data tape, routing edge cases to human reviewers. Different domains, same pattern—taking messy, important business processes and turning them into working products.
The problems I find most interesting are the ones where AI creates real operational leverage: cutting manual analyst cycles, improving data quality, turning unstructured documents into usable business data, and giving non-technical teams tools they actually rely on.
If you're building AI-enabled workflows, modernizing regulated operations, or turning manual expertise into scalable systems, I'd be glad to connect.
Let's talk about where AI can save you time and make your business run smoother. No pressure, no commitment.
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