Work-order support agents
Assistants that help interpret work-order details, surface the right next step, and reduce time spent hunting through documents and screens for context.
Custom GPTs and Copilot agents for operations
AI GPTs & Agents
Designing and building custom GPTs and Copilot agents around work orders and SLAs, grounded in approved content, scoped with guardrails, and paired with the enablement that makes teams actually adopt them.
Custom GPTs
Built for work-order and SLA support
Copilot agents
Grounded in approved operational content
Human-in-the-loop
Review kept in the workflow, not bolted on
Each build has its own page
Explore the featured agents and workflows
Click into an agent to see the full build: scope, MCP servers, architecture, guardrails, and pilot.
Featured GPT
HR Supervisor Coaching
An agent for frontline supervisors practicing difficult conversations, grounded in approved HR policies with HR review kept in the loop.
View the HR Supervisor Coaching buildFeatured agent
Smart Work Orders
A Copilot agent giving technicians one place to pull up a work order, diagnose an issue in plain language, and find the right SOP.
View the Smart Work Orders buildLatest build
Matter Intelligence Brief
A self-updating matter brief where every line has a source, every deadline is computed, and a lawyer approves what matters.
View the Matter Intelligence Brief buildApproach
Useful assistants start with the workflow, not the model
- An assistant is only as good as the content behind it, so source curation comes before prompt engineering.
- Operational AI has to fit the existing workflow. If a team has to leave their tools to use it, they will not use it.
- Accuracy is a review problem as much as a model problem, which is why human validation stays in the loop.
Work samples
What I built
Focused assistants for work-order and SLA workflows, with the scope, sourcing, and training needed to make them dependable.
SLA interpretation and response guidance
Agents that explain what an SLA commitment means in practice, including priority, response expectations, and escalation paths, in plain language for frontline teams.
Custom GPTs on curated content
Purpose-built GPTs grounded in approved process documentation so answers stay consistent with how the operation actually runs.
Guardrails and scope control
Clear scope, restricted source material, and defined boundaries so an assistant helps with what it knows and defers when it does not.
Enablement and AI literacy
Practical prompting guidance and training so teams learn where an assistant is reliable, where it is not, and how to review output before acting on it.
Pilot-first sequencing
Start with a narrow, high-friction workflow, measure whether it saves real time, then expand only where the value is visible.
Capabilities
What this enables
These describe the intended capabilities of the pattern rather than measured production results.
- Faster work-order handling
- Less time gathering context before action, with the relevant detail summarized up front.
- Consistent SLA answers
- The same commitment interpreted the same way across shifts, sites, and teams.
- Lower dependency on tribal knowledge
- Process understanding captured in a reusable assistant instead of living with a few people.
- A repeatable pattern
- One proven agent pattern that can be applied to the next operational workflow.
Let's architect your next transformation
Whether you're modernizing platforms, scaling AI, or rethinking your operating model — let's talk about what's possible.