Applied AI · Governed workflow automation
AI automation services
LLM workflows governed on your data and stack.
Personalised outbound, enrichment, support deflection, and ops automation — built and governed so AI sits inside Salesforce, HubSpot, Slack, and your warehouse — not as a random chatbot.
Operating model
Signal-to-outcome architecture
Business signals
CRM · channel · intent
Orchestration
Rules · AI · approvals
The operating problem
Fix the system, not one isolated symptom.
We start with the business outcome and trace the process, data, tools, and human decisions required to produce it reliably.
Generic AI that ignores CRM context
Addressed through architecture, automation, ownership, and measurable operating controls.
Manual enrichment and research
Addressed through architecture, automation, ownership, and measurable operating controls.
Support volume without deflection
Addressed through architecture, automation, ownership, and measurable operating controls.
No guardrails on model usage
Addressed through architecture, automation, ownership, and measurable operating controls.
Engagement model
From discovery to an operated system.
Map
Document the outcome, current process, data, constraints, and accountable owners.
Design
Define the architecture, decision rules, integrations, controls, and measurement plan.
Implement
Build the priority workflows, test failure paths, and validate real operating cases.
Operate
Monitor outcomes, document changes, and expand only after the foundation is stable.
What ships
A usable operating capability—not a slide deck.
Packaging: Build + govern AI workflows on client systems
Use-case and risk assessment
Configured for your stack, operating constraints, and internal ownership model.
Data and prompt architecture
Configured for your stack, operating constraints, and internal ownership model.
Human approval boundaries
Configured for your stack, operating constraints, and internal ownership model.
CRM and system integrations
Configured for your stack, operating constraints, and internal ownership model.
Evaluation and failure handling
Configured for your stack, operating constraints, and internal ownership model.
Runbook and ownership model
Configured for your stack, operating constraints, and internal ownership model.
Stack and governance
Designed to survive the handoff.
Works with enterprise apps, Zapier/Pabbly, data warehouses. We document ownership, exceptions, approvals, and the signals your team should monitor after launch.
Frequently asked
Before we design the system.
Can you work with our existing stack?+
Yes. AI automation services is designed around the systems you already operate, with replacement recommended only where the existing constraint is material.
Can we start with one workflow?+
Yes. A bounded, high-value workflow is usually the best way to validate the architecture, ownership, and measurement model.
Who owns the system after launch?+
That is defined during discovery. We can operate it, support an internal owner, or deliver a documented handoff with an agreed support model.
How do you measure success?+
We agree the operational and commercial measures before implementation, including cycle time removed, accuracy at handoff, human overrides, cost per completed task.
Start with the operating constraint
Design a practical ai automation services roadmap.
Bring the stack, process, and desired outcome. We will identify the highest-value place to begin.