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Frequently Asked Questions

How do you work with clients?

We meet every business where they're at. Everyone is going through this AI thing at a different pace, and that's fine.

We always start with a conversation. Whether you're actively looking for help or just trying to figure out the landscape, we want to hear where you are and what you're dealing with. We'll answer questions about what we do, how AI works, or whatever would be helpful.

If you need help thinking through the bigger picture (what AI might mean for your business, how to evaluate the technology, or whether an investment makes sense), we can help with that through a paid consultation.

If you have a real problem you want solved, we'll hold a discovery conversation to understand what you're trying to achieve so we can make a recommendation that fits your business goals. That might mean pointing you toward publicly available tools that are already accessible and effective. Or it might mean building something custom.

If we move forward with a build, we can structure it as a project-based pilot or as an ongoing fractional engagement. Pilots let you limit the investment, learn from it, and demonstrate benefit before committing to more. The fractional model works like having an AI team on retainer for ongoing support across multiple use cases.

What is a Managed Intelligence Provider?

Honestly, this is a brand new industry being born in real time. We're figuring it out as we go, just like everyone else.

But here's what it means to us: we're not just builders. We partner with businesses to help them use AI in ways that actually work for them.

That means helping you figure out where AI can help and where it can't. Building systems that fit your environment and ways of working. Making sure those systems work alongside your people, not against them. And then staying involved because AI systems need continuous care. They're doing work in collaboration with your team, and that requires ongoing attention.

It also means keeping an eye on what's coming. Things are changing fast in this space. New tools, new risks, new opportunities. Part of our job is to watch that for you and let you know what you might want to look at, what you might want to protect yourself from, or what you might want to take advantage of.

We're here for all of it. Strategy, execution, stewardship, and staying ahead of what's next.

How do we decide what to build?

We start with outcomes.

The first question is always: What are you trying to achieve? What problem are you actually trying to solve, and why does it matter?

From there, what we've found most helpful is to think about AI as roles rather than features or automations. We call these personas. Each one does a specific job in your business.

  • A Sales Intelligence Assistant that helps your team prepare for discovery calls by researching prospects and surfacing relevant context
  • An Operations Coordinator that monitors workflows, surfaces bottlenecks, and helps your team stay aligned
  • A Knowledge Steward that captures institutional knowledge and makes it accessible when people need it

Each persona has a primary skill (the core job it does) and supporting capabilities (the tools, integrations, and context it needs to do that job well).

This approach keeps things focused, makes adoption easier, and helps ensure you're building something that solves a real problem.

When it comes to prioritization, we've found it helpful to look at:

  • Business value (How much does this matter?)
  • Feasibility (Can we actually do this?)
  • Context availability (Do we have the information we need?)
  • Adoption likelihood (Will people actually use it?)

The goal is to find the clearest, highest-confidence path to value.

Can AI work with my existing systems?

Almost certainly, yes.

AI assistants work with your existing infrastructure. Your CRM, your project management tools, your Google Workspace, your databases, your internal processes. If you have an API or a way to access your data, we can integrate with it.

We've worked with GoHighLevel, Odoo, HubSpot, Google Workspace, and a range of other platforms. We add capability to what you're already doing without requiring you to rip anything out or rebuild your operations.

Voice agents can be made to work with most modern phone systems, so if you're looking to automate or enhance phone-based interactions, that's usually possible too.

Technology is also advancing fast. New tools are emerging that allow AI agents to perform work directly on a computer desktop, which opens up even more possibilities for integration and automation.

If there are constraints or dependencies that could affect what's possible, we'll surface those during discovery so you have a clear picture of what it takes to move forward.

What happens in discovery?

Discovery is where we figure out what you're actually trying to solve and how AI can best help you do it.

We cover:

  • Outcome: What are you trying to achieve, and why does it matter?
  • Context and Environment: What systems, data, and tools do you have? What would a person need to know if they joined the team?
  • Interaction and Workflow: How do people work today, and what's the best way for them to interact with an AI assistant?
  • Success Definition: What does success actually look like in practical, observable terms?
  • Risks and Constraints: What could go wrong? What are the boundaries?

By the end of discovery, we have what we need to come back with a clear recommendation or proposal that fits your specific situation.

Is AI safe to use in my business?

AI is a powerful tool, but it's not perfect. The results are never guaranteed to be accurate, which is why human oversight is critical. We design systems with that in mind.

Our approach to onboarding is built around trust. We don't just flip a switch and automate everything. We introduce the system gradually, test it in real scenarios, and make sure it's behaving reliably before we increase automation. You need to trust it before you depend on it.

We also build in anti-hallucination safeguards. That means designing prompts and context pipelines that reduce the likelihood of the system making things up, and putting guardrails in place to catch issues when they happen.

On the security and data privacy side, we take this seriously. We work with your existing security posture, follow best practices for API access and data handling, and make sure we're not creating new vulnerabilities in your environment. If there are specific compliance or security requirements, we'll address those during discovery.

The bottom line: AI introduces new capabilities, but it also introduces new risks. Part of our job is to help you manage those risks so you can use AI confidently.