WHAT I DO

Practical AI, built around your business.

From identifying what is worth solving to shipping reliable systems and enabling the team that will own them.

01 / DISCOVER

AI Opportunity & ROI Assessment

Start with workflows, data and economics—not with a model. Discover opportunities with business leaders, distinguish AI-worthy problems from simpler automation, and prioritize an achievable first initiative.

  • Business-process and pain-point discovery
  • Use-case ranking by feasibility, value and risk
  • Data/access readiness and existing-system assessment
  • KPIs, baseline measurements and pilot success criteria
Typical outcome Prioritized opportunity roadmap & pilot charter
02 / EMBED

Forward Deployed AI Engineering

Work directly with business stakeholders and internal developers to turn one bounded problem into a tested workflow. This is hands-on delivery, not a strategy presentation followed by a hand-off.

  • Embedded problem and workflow discovery
  • Architecture, implementation and technical mentoring
  • Integration with approved enterprise data and services
  • Testing, measurement, rollout and knowledge transfer
Typical outcome Working pilot or production candidate, owned by your team
03 / ENGINEER

Enterprise LLM & AI Implementation

Make generative AI useful beyond demonstrations, with retrieval, explicit evaluation, system integration, human review and production operations designed into the solution.

  • Grounded knowledge retrieval and document workflows
  • LLM integrations, model routing and deterministic checks
  • Evaluation sets, observability and quality monitoring
  • Access control, privacy-aware design and operating procedures
Typical outcome Tested, explainable and maintainable AI workflow
04 / OPTIMIZE

AI/ML for Business Operations

Explore quality, operational analytics, prediction and decision support where the underlying data and business need justify it. Partner with domain specialists as needed for highly specialized industrial modelling.

  • Operational and quality data feasibility review
  • Forecasting, anomaly analysis and decision support
  • Baseline comparison against simpler alternatives
  • Human-reviewed recommendations and impact tracking
Typical outcome Evidence-backed feasibility assessment or focused ML pilot
05 / ENABLE

Fractional AI & Engineering Leadership

For companies with internal IT talent that need experienced direction, delivery discipline and architecture judgment to move faster and reduce rework.

  • Technical strategy, architecture and roadmap reviews
  • Engineering execution and vendor evaluation
  • Coaching engineers, managers and technical leads
  • Governance, reliability, cost and operating-model guidance
Typical outcome Stronger delivery capability without permanent executive hiring
HOW AN ENGAGEMENT WORKS

Clarity before commitment.

01

Understand

Business goals, current systems, constraints and stakeholders.

02

Prioritize

Define a valuable, bounded pilot and measurable baseline.

03

Build & validate

Work with the team and evaluate against real cases.

04

Operationalize

Handover, governance and a credible path to scale.

LET'S BUILD WHAT WORKS

Have a business challenge worth solving?

Start with the problem, the economics, and what success would look like.

Start a conversation