Work
Projects built to solve real operational problems.
AI products, process automation, and systems integration — each one starting with a business problem, not a technology choice.
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Wolflow & Wolfpath — AI Readiness & Roadmapping
Two live tools that answer the questions organizations get wrong before adopting AI: whether a problem is ready, and where to start.
The problem
Organizations rush into AI adoption without a framework for deciding where it actually belongs. They invest in the wrong workflows, build tools nobody uses, and lose trust in the process. The gap isn't technical capability — it's structured decision-making at the front end.
What I built
Wolflow runs organizations through seven decision gates that surface whether a workflow is genuinely ready for AI — checking data availability, process stability, risk tolerance, and more — before anyone spends money or writes code.
Wolfpath takes it a step further: paste any workflow description and receive a prioritized adoption roadmap with effort estimates, impact scores, and sequenced next steps. It doesn't just identify opportunities — it tells you what to do first and why.7 decision gates before any AI investment< 5 min to a full prioritized adoption roadmap2 tools from concept to production in under 5 monthsStack
LLM Orchestration FastAPI Next.js Tailwind CSS Railway Cloudflare -
Wolfprompt — Auditable Prompt Routing Infrastructure
A four-dimension classification schema that routes prompts to the right model, logs decisions with full auditability, and brings structure to LLM deployments at scale.
The problem
As organizations deploy LLMs internally, they face a structural problem: every prompt goes to the same model with no routing logic, no cost optimization, and no audit trail. When something goes wrong — or when compliance asks what the model was told — there's no answer.
What I built
Wolfprompt classifies every prompt across four dimensions — intent, sensitivity, complexity, and domain — then routes it to the appropriate model tier with a full decision log. The result is lower inference costs, better output quality for each use case, and an audit trail that compliance teams can actually use.
Stack
LLM Routing FastAPI Classification Schema Audit Logging Next.js Railway -
Optiv — Risk Assessment Automation
Reduced vendor risk assessment processing from three months to one week by rebuilding the entire workflow around automation — without adding headcount.
The problem
300+ vendor applications. A three-month manual review cycle. Data scattered across SharePoint and ServiceNow, requiring constant re-entry and reconciliation. The bottleneck wasn't people — it was structure. Information arrived in the wrong format, lived in the wrong place, and required human intervention at every handoff.
Process flow — before & after
What I did
Mapped the entire workflow end-to-end, identified that the bottleneck was structural — not a capacity problem — and rebuilt data collection around structured SharePoint forms that automation could actually act on. Built Power Automate flows for risk categorization, approval routing, and ServiceNow ticket generation. Added executive dashboards for real-time visibility. Implemented validation rules at entry so errors never reached downstream systems.
Stack
SharePoint Power Automate ServiceNow PowerBI ExcelProprietary system — no public repository.
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Optiv — Executive Dashboard Integration
Unified Jira and ServiceNow data into a single real-time executive dashboard, collapsing a three-day manual reporting cycle into thirty minutes.
The problem
Two project management systems — Jira for Agile teams, ServiceNow for Waterfall — created a data silo that made portfolio visibility impossible without manual effort. Executives needed status reports, but getting them meant three days of CSV exports, pivot tables, and reconciliation across platforms. By the time reports landed in inboxes, the data was already stale.
Reporting cycle — before & after
What I did
Sat in leadership meetings long enough to understand which questions executives actually needed answered — not which questions were easy to answer. Built API integrations pulling live data from both Jira and ServiceNow into a SharePoint data warehouse, with Power Automate handling scheduled refreshes and error recovery. PowerBI dashboards with department-level filtering, project health indicators, and one-click drill-down. The dashboard became the go-to resource for cross-departmental planning because it answered the right questions.
3 days → 30 min reporting cycle reductionLive data replacing stale weekly exports2 systems Jira + ServiceNow unified into one viewStack
PowerBI Jira API ServiceNow API SharePoint Power AutomateProprietary system — no public repository.
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ClaimGuard — Healthcare Claims Validation
AI-powered billing validation that catches complex clinical logic errors missed by traditional rule-based systems — from concept to working prototype in under 8 hours.
The problem
Healthcare providers lose billions annually to billing errors — gender-procedure mismatches, age-inappropriate treatments, anatomical inconsistencies — that slip through manual review. Traditional rule-based validation catches obvious violations but can't reason about clinical logic. It flags the error without explaining why it matters or what the downstream consequences are.
What I built
A validation platform combining deterministic rules with AI medical reasoning. Five parallel workers process claims simultaneously, with LRU caching for similar claim patterns. Each flagged claim gets a clinical explanation, business impact analysis, financial estimate, compliance assessment, and prioritized next steps — not just an error code.
< 8 hrs concept to working prototype5× speed via parallel processing85% API cost reduction via LRU cachingStack
Python OpenAI GPT-4 Streamlit FastAPI LRU Cache Parallel Processing
Additional projects
Wolfstitch Cloud
SaaS platform converting 40+ document formats into AI training datasets. Zero data retention, sub-10s processing, enterprise-grade privacy.
Wolflow AI Toolkit
Open-source local AI development tools: Wolfkit (safe code testing), Wolftrain (local LoRA fine-tuning), Wolfstitch Desktop (offline dataset prep).