Practical AI, done with care
Rabb Consulting Group is an independent AI consulting practice focused on helping enterprise and public-sector teams build GenAI capabilities that are responsible, explainable, and built to last.
I work at the intersection of AI delivery, responsible use, and team enablement. My background spans federal research support, enterprise consulting, and hands-on pipeline development.
Why this practice
Most AI consulting falls into one of two traps. The first is selling a framework: a polished deck, a maturity model, and a recommendation that someone else has to implement. The second is pure technical delivery with no regard for whether the team can maintain it, explain it, or trust it.
Rabb Consulting Group exists because there is a better version of this work. One that connects strategy to delivery, that treats responsible AI as a practice rather than a checkbox, and that ends with a team that is more capable than when we started.
That means being honest when something is not a good use of AI, writing documentation alongside the code, running workshops that actually change how people work, and designing systems that hold up in production, not just in a demo environment.
Working principles
Outcomes over deliverables
A report that sits in a drive is not a result. The work is only done when something has changed: a team knows more, a pipeline is running, a process has improved.
Responsible by default
Attribution, audit logging, content safety, and ethical evaluation standards are not optional additions. They are part of how the work gets done.
Honest about tradeoffs
Every technical decision involves tradeoffs. The job is to make those visible and to help teams make good choices, not to oversell what AI can do.
Your team owns the output
Every engagement includes documentation, training materials, and a deliberate handoff. Building dependence on outside help is not a good outcome for anyone.
Experience
Experience includes work supporting MITRE, Vanguard, and Marriott. Client names listed for context; details available upon request.
- Lead workshops and hands-on tutorials in Python and AWS Athena for analysts and engineers on responsible GenAI use
- Develop NL-to-SQL and Python pipelines and training materials to accelerate research insights while ensuring compliance and ethical AI integration
- Design and publish guidance on attribution, audit logging, and content safety
- Conduct outreach and consultations with mission owners to align AI services with evolving needs and responsible-use standards
- Deliver observability dashboards to track usage, performance, and adoption of AI systems
- Led workshops and training for ML teams on recommendation models, transparency, and ethical evaluation (Marriott)
- Created instructional prototypes and workshops to teach predictive model outputs and responsible ML practices to product and UX teams (Vanguard)
- Developed guides, demos, and one-on-one support to improve ML literacy and empower non-technical stakeholders
Technical capabilities
Want to work together?
Start with a 20-minute call. Come with your problem, your team context, and your constraints. We will go from there.