> consultant. engineer. builder.
Business AI implementation for teams that need practical systems, not AI theater.
I help businesses identify, design, and implement AI workflows using LLMs, automation, endpoints, and agentic systems where they actually make operational sense.
Start with the workflow, bottleneck, and business outcome before choosing AI tools.
Use hosted or API-accessible models inside structured business flows and automation systems.
Design validation and handoff points so AI improves work without hiding risk.
What I offer
Services built around practical AI adoption.
Each service is designed to move a business from vague AI interest toward a useful workflow, working prototype, or documented implementation path.
01
AI Workflow Assessment
A structured review of your process to identify where LLMs, automation, or agents can realistically improve speed, quality, or consistency.
- Workflow map and bottleneck review
- AI opportunity list with priority ranking
- Risk, complexity, and implementation notes
- Clear roadmap for what to build first
02
AI Automation & Agent Workflow Implementation
Design and build AI-assisted workflows that connect LLM calls, prompts, tools, data, routing logic, validation steps, and human review.
- LLM endpoint and API workflow design
- Task-specific agents where they fit the process
- Multi-agent workflows only when they add real value
- Automation handoffs, logging, testing, and support notes
03
Custom AI Assistants & Knowledge Systems
Focused internal assistants that help teams find answers, summarize information, reuse knowledge, and follow repeatable processes.
- Internal knowledge and documentation review
- Assistant behavior, scope, and instruction design
- Retrieval/search workflow planning
- Testing examples and deployment guidance
04
AI Implementation Documentation & Training
The playbooks, documentation, prompts, and training materials teams need to use AI systems consistently after they are built.
- SOPs and workflow guides
- Prompt patterns and usage examples
- Technical documentation for AI workflows
- Team enablement and implementation notes
Common use cases
Where business AI usually creates leverage.
- Document classification and extraction
- Research and summarization workflows
- Customer support triage
- Internal knowledge assistants
- Reporting and analysis workflows
- Proposal, brief, and SOP generation
- Content operations and repurposing
- Process documentation and QA
How I work
A practical implementation path.
- MapClarify the workflow, users, inputs, outputs, and constraints.
- PrioritizeChoose use cases based on value, feasibility, risk, and maintainability.
- DesignDefine the prompt, model, tool, data, review, and handoff architecture.
- BuildImplement the workflow, prototype, automation, assistant, or technical resource.
- ImproveTest, document, measure, and refine based on actual usage.
Ways to engage
Start small, build what works, and scale carefully.
AI Workflow Opportunity Audit
1–2 weeks
Best for teams that know AI could help but need a clear, practical starting point.
Output: workflow map, opportunity ranking, and implementation roadmap.
AI Workflow Build Sprint
2–6 weeks
Best for teams with a known process to improve or a high-value workflow to prototype.
Output: prototype or production-ready workflow, depending on scope.
AI Systems Partner
Monthly
Best for teams building multiple AI workflows and needing ongoing implementation support.
Output: continuous workflow improvement, documentation, and implementation guidance.
Next step
Have a workflow that feels repetitive, messy, or hard to scale?
Start with the business process. Then we can decide whether an LLM, automation, agent, assistant, or documentation system is the right tool.