From business friction to an AI-enabled workflow
Redesign the process. Build the capability. Make it work in practice.
We begin with the business outcome, map how the work happens today, design a clearer future-state workflow, and build the AI capabilities required to operate it.
The four phases
1. Understand
We study the objective, the current workflow, the information required, the systems involved, and the points where time, context, or accountability are lost.
— Leadership and process-owner interviews
— Current workflow mapping
— System and document review
— Knowledge and data readiness
— Baseline and success criteria
Example — Weekly report: we watch how your manager assembles it today, pulling numbers from chats, spreadsheets, and email.
2. Redesign
We improve the process before choosing the technology. Each step is assigned to the appropriate actor: a person, a deterministic rule, conventional software, an AI copilot, or an agent.
— Steps to remove or combine
— Human judgment to preserve
— Information to structure
— Approval and exception paths
— Future-state workflow
— Measurement plan
Example — Weekly report: we define what it should contain, where each number comes from, and who signs off.
3. Build, automate, and connect
We build the required work environment, add AI assistants, automations, or agents where they create value, and connect existing products where buying is more reliable than building.
— RAG and source-grounded output
— APIs and tool integrations
— Structured extraction and outputs
— Agent tools, state, and memory
— Role-based access and approvals
— Evaluation and fallback behavior
Example — Weekly report: we connect the sources, let AI draft it on a schedule, and add a human approval step.
4. Deploy and improve
The system is tested with real users and representative cases before broader rollout. We monitor reliability, exceptions, adoption, cost, and business outcome, then improve the workflow and expand only when the evidence supports it.
— Pilot and user testing
— Evaluation set and success thresholds
— Role-specific training
— Operating SOP and escalation path
— Monitoring and observability
— Ongoing improvement
Example — Weekly report: every Monday your manager reviews a ready draft instead of assembling it from scratch.
Simple automations can often be prototyped in hours. A focused first version can reach real use within days. Production timelines depend on integrations, permissions, testing, and exception handling.
Begin with a free AI Fit Call
The first conversation is free and takes 20–30 minutes. It is designed to understand the business problem, determine whether AI is relevant, and recommend the right next step — a working session, an assessment, a build, or no project at all.
If the problem needs focused working time before an assessment makes sense, the $175 AI Adaptation Session (60 minutes) provides practical recommendations, a recording, and an action list. The session fee is credited toward an Assessment booked within 30 days.
The service ladder: Fit Call (free) → Session ($175) → Assessment ($750 launch) → Build ($1,500–$6,500) → Care ($500/month)
AI Workflow Assessment
The AI Workflow Assessment is completed within 3–5 business days after the intake materials are received. Launch price: $750 ($950 standard), agreed as a fixed scope before work begins. The fee is credited toward an implementation project of $3,000 or more approved within 30 days.
What you receive
— Executive Summary
— Current Work Environment Map
— Prioritized AI Opportunity List
— Top Three Opportunities
— Recommended First Implementation
— Future-State Workflow
— Tool and Knowledge Recommendations
— Build / Buy / Connect Recommendation
— Initial Business-Impact Estimate
— Fixed-Scope Implementation Proposal