Agentic workflow systems
Autonomous AI agents that handle intake, routing, qualification, documentation, follow-up, and reporting, with no manual intervention between steps.
Service: Build & Deploy
We Ship. We Don't Pilot.
You don't need another proof of concept. You need a system that runs in production: integrated with your tools, handed off to your team, and producing output on day one.
The problem
The gap between "AI works in theory" and "AI works in our business" is wider than most people expect.
Most AI implementations stall in the middle. The tool works in the demo. The pilot shows promise. Then it hits your actual workflows (the specific handoffs, the edge cases, the data formats, the team habits) and it starts to slip.
Not because AI can't do it. Because the system wasn't built for how your business actually runs. It was built for how a generic business is supposed to run.
Custom builds are different. They start with your operation (your tools, your team structure, your data, your process) and build around it. The system that comes out the other side doesn't ask your team to change how they work. It fits into how they already work, and it does the parts that shouldn't require a person.
That's the difference between a pilot that fades and a system that runs.
Production systems, built around your operation.
Not a list of tools to buy. These are the systems I build for small businesses and startups, usually 5 to 100 employees: working software that runs in your environment and does the operational work your team shouldn't have to.
Sales pipeline automation
Lead capture, CRM integration, qualification logic, follow-up sequencing, and pipeline reporting that runs without your sales team manually moving deals through stages.
Executive dashboards
Real-time operational intelligence that pulls from your existing tools and surfaces what leadership needs to see, without someone rebuilding a report every Monday morning.
Custom AI assistants
GPT-based tools trained on your business: your SOPs, your product knowledge, your customer language. Built for your team, not for a general audience.
Integrated tech stack builds
Connecting the tools you already use, from CRM to communication to project management to documentation, into one system with AI handling the handoffs between them.
Compliance and documentation systems
AI-powered documentation workflows for regulated industries that hold their accuracy without the manual drafting time.
Four steps from scope to a system that runs.
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01: Scope & Design
We design the system around your operation.
We start from your AI Opportunity Map, whether it came from the audit or we define it together. I design the system architecture: what it does, what it integrates with, what the handoffs look like, and the success metrics we track.
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02: Build
I build it in your environment, wired into your stack.
The system gets built where it will actually run, integrated with the tools you already use from the start. You're involved at key milestones, not just at the end.
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03: Test & Refine
We run it against real data before handoff.
We test the system against real data and real workflows before it goes live. Edge cases get handled. Logic gets tightened.
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04: Deploy & Handoff
It goes live, and your team owns it.
The system goes live and your team gets trained on it. You get documentation covering what it does, how it works, and how to adjust it. I stay available for a post-launch optimization window.
Deliverables
What you walk away with.
- Custom system architecture designed around your operation
- Full build: agentic workflows, integrations, and automations, in your environment
- Integration with the tools you already use
- Testing against real data before launch
- Team enablement and onboarding
- Handoff documentation
- A post-launch optimization window
Delivered as: a working system in your environment, plus documentation and a live handoff to your team.
Investment
Build & Deploy: scoped to your project.
Every build is different. Pricing starts at $5,000 for single-system builds and scales from there, based on what the system has to do and what it connects to.
Most clients come to Build & Deploy after completing an AI Opportunity Audit. If you don't have an Opportunity Map yet, that's the right place to start: it defines the scope of the build and prevents expensive changes mid-build.
Once the first system is running and proving itself, Scale & Stack is how you build on it, one system at a time.
Timeline: single-system builds run 2–6 weeks. Multi-system builds are scoped up front.
Two builds, both running in production.
Energy incentive & audit firm
Built an agentic workflow system that handles lead intake, qualification, sales pipeline routing, and handoffs across a utility rebate operation. The systems run in production, and the manual intake process is gone.
Real estate investment firm
A full business audit followed by a custom agentic build: buyer-facing intake systems, a custom comparables engine, sales department automation, and an executive dashboard pulling real-time operational and sales data.
Client names anonymized. The systems described are running in production. Named references available on request.
Common questions.
Do I need to complete the AI Opportunity Audit first?
Not required, but recommended. Clients who come in with a clear, validated opportunity skip the audit. Clients who aren't sure where to start do the audit first. It prevents expensive course corrections mid-build.
How long does a typical build take?
Single-system builds run 2–6 weeks. Multi-system builds take longer. The timeline is defined during scoping, before any work starts.
What tools and platforms do you build on?
Tool-agnostic. Common stacks include HubSpot, Salesforce, Monday.com, Asana, Notion, Slack, Zapier, Make, the Google Workspace and Microsoft 365 suites, Adobe, and Claude Code, plus custom GPT deployments. I build on what your team already uses wherever I can.
Will my team need to change how they work?
The goal is the opposite. I build around how your team already works, so the system takes the operational drag off their plate instead of adding a new process to learn.
What happens after the build is live?
A post-launch optimization window while the system settles into real use, then documentation so your team can operate it independently.
Can you build on top of AI systems someone else started?
Yes. Scoping takes a little longer because I have to understand what's already there, but it's not a blocker.
Get started
Ready to stop piloting?
One 20-minute call. I'll tell you what's worth building first, what it takes to ship it, and what it looks like running in production. No deck. No pitch.