Real answers, real numbers.

Start with your foundation, not your tools. Most businesses jump straight to buying AI software before they've captured how their business actually works. That's backwards. The first step is documenting your context: who you are, what you do, how decisions get made, and where time gets lost. Once that's in place, AI has something to work with. Without it, you're just adding another tool to the pile.

You don't need perfect data, but you do need captured knowledge. Depending on the size of your organization and the amount of data you have, data cleanup is necessary to achieve certain outcomes. Regardless, what you need is your institutional knowledge written down in a way AI can reference: your processes, your priorities, your decision-making patterns. We build that with you in the first week. As the system starts running it is possible data cleanup becomes easier because you can see where the gaps are.

AI tools solve one problem at a time. An AI Operating System connects everything. A tool might help you write emails or schedule posts. An Operating System knows your business context, connects to your existing software, and runs workflows that actually match how your team works. Tools sit on top of your business. An Operating System becomes the layer your business runs on. The difference is whether AI is an add-on or infrastructure.

The Foundation Install starts at $5,300 for teams of 1-10 people. That's a flat fee for a complete AI Operating System: your context captured, your tools connected, and 2-3 workflows running. Larger teams are scoped on a discovery call. After the install, ongoing expansion runs $1,500-4,500/month depending on whether you want us to maintain it, train your team, or stay embedded as a partner. We publish real numbers because you shouldn't have to book a call just to know if you can afford it.

While every team is different, ROI comes in the form of time, energy and resources recovered, not outputs generated. The question isn't "how many emails did AI write?" It's "how many hours did my team get back, and what did they do with them?" Another common ROI is needing fewer software platforms or eliminating other things that are being paid for entirely. Most clients see measurable results within 60-90 days. The biggest win many of our clients share is improved clarity and buy-in on AI.

You get a working AI Operating System configured for your business. Specifically: a Context Layer (10-15 files capturing how your business works), a Connections Layer (your existing tools wired together through one interface), and a Workflows Layer (2-3 automated processes running by the end of week two). You also get your team trained to use it. This isn't a strategy document or a recommendations report. It's infrastructure you operate the day we hand it over.

About two weeks. Week one is building your Context Layer: a guided session where we capture how your business actually runs. Week two is connecting your tools and configuring your first workflows. This is an install with a defined timeline, defined deliverables, and a working system at the end that we can help you expand on over time.

You'll use Claude as your primary interface, which works like a conversation. You ask questions in plain English, and the system responds using your business context. If your team can send a text message, they can use this. The learning curve takes practice but is manageable. We handle the technical configuration so you can focus on using it.

We build AI capability, not dependency. After the Foundation Install, your team knows how to use the system, update the context files, and request new workflows. Some clients run it themselves from there. Others stay on a monthly retainer where we maintain it, add new capabilities, or train additional team members. Either way, you own the system. It runs on your accounts, with your data, under your control.

Someone on your team catches it before it reaches a customer. We design workflows with human checkpoints on anything that's customer-facing or involves money. AI drafts, routes, and prepares. A person approves before it goes out. The goal isn't full automation. It's removing the repetitive work so your team can focus on the decisions that actually matter. Mistakes happen when AI runs unsupervised. We don't build unsupervised systems.

Most AI investments are one dimensional. This is infrastructure. Instead of a single tool that solves one problem or a training that teaches concepts but no direct application to your business, your Foundation Install becomes the AI layer your team and business run on. It compounds over time because every workflow we add builds on the foundation we've already installed. You're building an asset that gets more valuable the longer it runs.

Because we build the foundation before the features. Most AI experiments fail because they start with a tool instead of starting with context. The tool doesn't know your business, so it gives generic outputs. Your team gets frustrated. The tool gets shelved. We start with your business knowledge captured and your systems connected. When AI has that foundation, it stops feeling like an experiment and starts feeling like infrastructure. That's why it sticks.