AI Strategy and Discovery
Identify the highest-value AI and GenAI use cases for your business, scope a defensible POV, and build a roadmap from prototype to production. Executive-fluent technical leadership, no slideware.
Joseph M. Hahn, Ph.D.
Ph.D. scientist with 12+ years architecting and deploying production AI, machine learning, and generative AI for enterprise clients across many industries. Available for select project and fractional engagements, from small shops to the enterprise.
About
I spent eight years at Oracle delivering AI engagements across manufacturing, oil and gas, finance, public sector, real estate, and retail. That work spanned customer discovery, technical design, and Pilot-to-production (P2P) delivery. Before Oracle, four years delivering big-data ML systems on Hadoop for enterprise clients. And before that, two decades of peer-reviewed research in computational physics and planetary dynamics.
That combination is the differentiator: I am a scientist who can sit with the C-suite on Monday and write the production code on Tuesday. I work directly with technical and business stakeholders to turn ambiguous AI ambitions into systems that actually run.
As an independent practice, I work across industries and recommend the architecture that actually fits your problem. Vendor-neutral by design.
Services
For small firms putting their data to work, and enterprises sharpening their AI delivery. Engagements range from one-week strategy sprints to multi-month production builds, as embedded consultant, part-time AI lead, or in-house advisor.
Identify the highest-value AI and GenAI use cases for your business, scope a defensible POV, and build a roadmap from prototype to production. Executive-fluent technical leadership, no slideware.
Independent technical review of AI and ML vendor proposals, RFP responses, and platform pitches. Cuts through the marketing to tell you whether the architecture, data assumptions, integration story, and pricing actually hold up. Vendor-neutral by design.
Retrieval Augmented Generation (RAG), natural-language-to-SQL (NL2SQL), prompt engineering, and GPU deployment. Each built as the right tool for the specific use case.
Production single-agent, multi-tool systems that combine RAG, NL2SQL, web search, to provide access to structured and unstructured data behind one conversational interface. Designed for enterprise data, not toy demos.
Supervised ML on tabular and time-series data: predictive maintenance, opportunity & risk scoring, forecasting, fraud detection, resource optimization. End-to-end pipelines from data and feature engineering through model deployment & monitoring.
Underlying stack: Python, SQL, pandas, scikit-learn, LLM APIs, vector search,
Spark, Hadoop ecosystem, Jupyter, Git, Claude Code, plus a deep Oracle
Cloud Infrastructure stack (Oracle Database, Oracle Autonomous AI
Database, OCI Data Science, OCI Generative AI service),
as well as conceptual
fluency across the major hyperscalers.
Engagements
Hourly project work, or monthly retainers with reserved hours.
$250 / hour
Fixed-bid pricing available for well-scoped projects. Standard response time; for priority access, see the retainer tiers.
Best for: a strategy sprint, vendor review, POV build, custom RAG or Agent integration, or any ML or data analysis task.
$350 / hour
$3,500 / month. Hours do not roll over. Monthly commitment, cancel anytime.
Best for: small teams that want consistent senior AI input on a lighter cadence.
$300 / hour
$6,000 / month. Hours do not roll over. Monthly commitment, cancel anytime.
Best for: teams who want a senior AI advisor continuously engaged with their stack and roadmap.
Custom engagements welcome. Multi-month projects, advisory boards,
and
equity arrangements considered for the right fit.
Selected Work
Predictive ML for industrial process safety
Supervised ML on sensor time-series, alerting operators before dangerous conditions develop on the production floor.
ML for local-government revenue workflows
Supervised regression on tabular records, supporting a local-government revenue-collection workflow.
GenAI workflow automation for a public-sector case-management team
Auto-drafting structured reports from unstructured intake, dramatically reducing analyst time per case.
Natural-language analytics for a specialty e-commerce retailer
NL2SQL self-service analytics that lets non-technical business users query merchandising and sales data in plain English.
Media research AI agent with web search
AI agent with multiple tools that automates ongoing data collection from web sources for downstream analysis.
Capabilities
The working stack that I use in production today.
Get in touch
Tell me what you are trying to build or untangle, and I will reply within one business day with a candid read on fit, scope, and next steps. No obligation.
Email Joe