AI built for operators, not platforms.

I design and build AI systems for the operating layer of a business. Workflows that run on actual work, automations across the tools the team already uses, and custom agents scoped to a single function.

 

The standard build is an isolated agent. One agent, one job, scoped tight enough that it cannot drift. When the work needs to move across multiple agents, I build the handoff instructions between them. The agents do the passing. I do not build orchestration platforms.

 

What I build, and what I will not.

• Workflows. Sequenced multi-step processes that take a real input and produce a real output. Documented, version-controlled, runnable. Built to be operated by the team, not by me.

• Automations. Cross-tool integrations across the systems already in use. Microsoft 365, Copilot and its plugins (Power Automate, workflows, Power Apps), SharePoint, Outlook, Teams, Google Workspace, HRIS, ATS, ticketing, project management, whatever the team relies on. The work moves between tools without manual handling.

• Isolated agents. Single-purpose agents built in Claude for one defined function or a complex multi-step workflow inside a single function. Process execution, drafting, cross-document comparison, auditing, reviewing, classifying, summarizing, screening. Built to hold under load and scoped tight enough that they do not drift.

• Multi-agent handoffs. Instruction sets that let isolated agents pass work to one another for end-to-end execution across a longer process.

• Dashboards and interfaces. Custom dashboards and multi-level workflows built in Claude, Lovable, and Base44 for the teams who will actually use the agents. The agent runs the work. The dashboard makes it visible, controllable, and operable by people who do not live in a terminal.

• Anything bounded. If the function is real, the inputs are clean, and the output is measurable, it can be built. Platform is a downstream decision. The build standard is the same.

• What I will not build. Orchestration platforms. Speculative R&D. Pilots with no production path. Demos that cannot run in production.

 

The stack.

• I build on Claude Enterprise. The choice is deliberate. Enterprise-tier security, zero data retention for client work, no model training on inputs, SOC 2 compliance, and the access controls that let me operate inside a regulated environment without exposing client data to the consumer surface. The work Mack Point requires that posture by default.

• For client-facing applications and interfaces, I also build on Lovable and Base44. App-builder platforms with AI-assisted frontends, useful when the deliverable is a tool the team will actually open and use, not a workflow that runs in the background. Security posture for app-builder work is scoped per engagement, since the build platform sits closer to the surface than the underlying data layer.

• Everything I build is accessible through the client's own environment. The work lives in the organization's accounts, on the organization's infrastructure, under the organization's access controls. Nothing routes through a personal Mack Point environment. Nothing depends on my login. The client owns what they paid for.

• I also consult on Microsoft Copilot, Power Automate, workflow tooling, and the plugins that connect them. For organizations already inside the Microsoft stack, the question is rarely which platform and more often how to get the platform you already pay for to actually run the work. That is the consult.

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Where the build ends.

Every Mack Point engagement closes with a handoff guide for the client's internal team. The guide documents what was built, how it runs, where it touches other systems, what to watch for, and how to extend it. The intent is that the team owns the work after I leave. No retainer required, no maintenance dependency built in.

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When the work is bigger than Mack Point's lane, orchestration platforms, large-scale multi-agent infrastructure, or enterprise rollouts that need a dedicated build team, I refer to a vetted third-party builder. I will not take an engagement I cannot ship well.

 

How the work is scoped.

Every build starts with a function, not a feature request. We diagnose where the function breaks under load, what the rate-limiter is, and whether AI is the right tool. If it is, we scope the smallest build that solves the problem. If it is not, I will say so on the call.

 

• The discipline behind the work is the STAR-T Sweep℠, the kaleidoscope framework I originated, pressure-tested in client engagements, and published.

• AI does not replace domain expertise. It accelerates the work of operators who already have it. — STAR-T Sweep℠

 

Engage Mack Point.

The first conversation is a thirty-minute fit call. We discuss the function, the load, and whether an AI build is the right move.

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