Assess
Understand the workflow, people, tools, data, and pain points before recommending an agent.
Output: Workflow map and agent scope.
We start by understanding one real workflow, then scope and build a practical AI agent that supports follow up, visibility, analysis, or coordination.
The service model is intentionally practical. We start with a focused workflow review, build around real tools and data, then support or expand what proves useful.
Understand the workflow, people, tools, data, and pain points before recommending an agent.
Create a working AI agent around one real process, such as maintenance, inventory, scheduling, reporting, quality, or coordination.
Tune the agent, monitor results, update prompts or rules, and adjust the workflow based on actual use.
Expand into connected workflows, integrations, and multi-agent coordination when the first use case proves value.
Assessment, prototype build, integration, and ongoing support are defined after workflow mapping, integration review, and implementation scope. No public dollar anchoring, no fixed product catalog.
One workflow reviewed with the people who own it, including data sources, users, approval points, and a written agent scope.
Built against real examples from your operation, tuned with your team, and documented so the workflow remains understandable.
Connects to forms, spreadsheets, databases, email, dashboards, ERP, MES, or lightweight workflow tools where appropriate.
Defined based on support needs and number of active workflows, including updates, usage review, and small improvements.
Pick one workflow that is slowing your team down. We will map it together, scope a practical agent, and quote the work. No platform commitment.