AI automation consulting for real operational workflows.
I help teams turn repetitive work, fragmented data, and manual handoffs into scoped AI systems. Every engagement starts with a fixed-fee roadmap so the proof, architecture, implementation scope, and price are clear before build work begins.
This is built for buyers who need more than AI advice: workflow automation, custom AI development, data pipelines, agent orchestration, and operator control designed around the way the business actually works.
AI automation should map to an operating system, not a loose demo.
The useful scope depends on the workflow. These are common patterns that can be validated in Phase 1 before deciding whether to build.
Revenue workflow automation
Turn research, account signals, review data, and CRM context into repeatable sales or customer workflows.
Data and intelligence pipelines
Collect, normalize, enrich, and monitor operational data so teams can act on trusted signals instead of manual exports.
Agent workflow orchestration
Route intent, call tools, draft outputs, and move work through approval queues with explicit failure handling.
Human-reviewed AI systems
Keep operators in control with review states, source trails, confidence notes, and clear handoffs before external actions.
Good fit when
There is a repetitive workflow with a clear owner.
The team can name the data sources, tools, and handoffs involved.
The output needs to land somewhere operational: CRM, dashboard, queue, report, or approval flow.
A narrow proof of concept would make the build decision easier.
Probably not the right fit when
The request is still a broad AI strategy conversation.
There is no data source, workflow owner, or business outcome.
The desired result is a generic chatbot that an existing SaaS tool already covers.
Procurement or security constraints are too unclear to scope responsibly.
A fixed-fee first step before custom implementation.
Phase 1 is designed to reduce delivery risk. You get enough proof and architecture to make a build decision before committing to a larger implementation.
Map the workflow
Define the current process, owner, data sources, decision points, review needs, and downstream systems.
Prove the riskiest part
Build a narrow proof of concept around the part most likely to determine whether the larger system is worth building.
Scope the implementation
Deliver the architecture, integration map, risk notes, timeline, and fixed-price Phase 2 proposal before build work begins.
Common questions
What does an AI automation consultant do?
I map the workflow, identify the useful automation boundary, define data and integration requirements, prove the riskiest part, and scope the implementation before build work begins.
Is this custom AI development or consulting?
Both, but in order. Phase 1 is consulting, architecture, and proof. Phase 2 is custom AI development once scope, price, and delivery risk are clear.
Can the system integrate with existing tools?
Yes, if the APIs, permissions, and workflow boundaries are clear. Common targets include CRMs, dashboards, databases, inboxes, forms, and internal tools.
What if the roadmap proves we should not build?
Then the roadmap did its job. You keep the blueprint and proof of concept, and you can pause, reduce scope, buy a simpler tool, or build internally.
Have a workflow worth scoping?
Start with the Systems Audit. I will review whether the workflow has enough ownership, data access, and business value to justify a Phase 1 Roadmap.