Resources
Practical tools and plain-language guides to help you think clearly before you build or buy anything.
Guides
The AI Buyer's Guide for Small Business
Know the right questions, avoid the wrong vendors, and make a decision you won't have to undo. Written for business owners, not technologists.
Use AI Like A Pro — A Practical Guide for Solopreneurs and SMBs
A practical, no-fluff guide to putting AI to work in your business — written for solopreneurs and small business owners who are ready to move.
AI Readiness Assessment
10 questions. Plain language. An honest result with a clear next step — specific to where you are right now.
Question 1 of 10
When you hear the phrase "AI for your business," what is your honest first reaction?
Question 2 of 10
How would you describe the current state of your business processes — the way things actually get done day to day?
Question 3 of 10
Think about the most repetitive task in your business right now — the one that takes time every week without requiring your real expertise. What best describes it?
Question 4 of 10
Have you tried any AI tools in your business — ChatGPT, a chatbot, an automation tool, anything?
Question 5 of 10
How do leads currently find out about your business — and what happens after they do?
Question 6 of 10
If an AI system made a mistake while representing your business to a client — gave wrong information, made a promise you can't keep — how prepared are you to handle that?
Question 7 of 10
How would you describe your comfort level with technology in general?
Question 8 of 10
What is the biggest thing holding your business back from growing the way you want it to?
Question 9 of 10
How do you feel about the idea of AI representing your business to a potential client — answering questions, qualifying them, routing them to the right next step?
Question 10 of 10
When you think about investing in AI infrastructure for your business, what best describes where you are?
5 Questions to Ask Any AI Vendor
The AI sales cycle is aggressive right now. Vendors are everywhere — at conferences, in your inbox, on LinkedIn, in your referral network. Some of them are building real things. Some of them are repackaging a ChatGPT wrapper with a logo on it and calling it enterprise software. You do not need to be a technologist to tell the difference. You need five questions.
01
Where does my data go — and who owns it after the engagement ends?
This is the first question and the most important one. Ask it before anything else. Some AI products are trained on your inputs. Some store your conversations. Some share data across their customer base. Some are built on third-party infrastructure with their own data policies that the vendor has no control over. What you need to know: does your proprietary information — your client data, your pricing, your internal processes — stay yours? Where is it stored? Who has access to it? What happens to it if you cancel? If the vendor cannot answer this clearly and in writing, that is your answer.
02
What does it do when it doesn't know something?
This question separates real AI implementations from dangerous ones. Every AI system will eventually encounter a question outside its knowledge base. What happens next is a design decision — and it tells you everything about how carefully the system was built. The right answer: it says so, clearly, and routes to a human or a defined next step. The wrong answer: it generates something confident and incorrect. That is called hallucination, and in a customer-facing system it means your business is on record for whatever the AI said. Ask to see it fail. If the vendor won't show you an edge case, they are not confident in their guardrails.
03
What does implementation actually look like — and what do you need from me?
"We'll have you up and running in 48 hours" is a red flag, not a selling point. A real AI implementation requires your input. Your services. Your pricing. Your escalation paths. Your voice. Your edge cases. Your industry-specific knowledge. If a vendor is not asking you for any of that before they promise a deployment timeline, they are not building something for your business. They are installing something generic and hoping you don't notice. Ask specifically: what information do you need from us, how long does onboarding actually take, and what does the training process look like? The depth of that answer tells you whether they understand your business or just their own product.
04
What does success look like — and how will we measure it?
If a vendor cannot define success in measurable terms before the engagement begins, they are not accountable to any outcome. Push for specifics. Not "improved efficiency" — what metric, what baseline, what target, what timeline? Not "better customer experience" — what does that mean in a number you can track? The businesses losing money on AI right now signed contracts with vague promises attached. The ones winning have documented outcomes with defined milestones. Make the vendor define what winning looks like before you write the check.
05
Who builds it, who owns it, and what happens if we part ways?
This question has two parts and both matter. First: who is actually doing the work? Is it the person you are talking to, or is it being outsourced to a team you will never meet? That is not a judgment — it is a risk assessment. You need to know who is accountable. Second: when the engagement ends, what do you walk away with? Do you own the knowledge base? The accounts? The trained model? The codebase? Or does the AI live on their platform, accessible only as long as you keep paying? Ownership at the end is not a detail. It is the whole point. An AI system you cannot access, modify, or take with you is not an asset. It is a dependency.
The vendors worth working with will not flinch at any of these questions. They will answer them directly, in writing, before you sign anything. The ones who get defensive or vague when you push — that response is the information. Stay savvy.
AI Glossary
No textbook definitions. No jargon dressed up as explanation. Just what these words actually mean for your business.
AI Agent
A system that can take action on its own — answer questions, route requests, escalate to a human — without being manually operated for every interaction. Not a chatbot. A chatbot follows a script. An agent reasons.
Large Language Model (LLM)
The underlying technology that powers most AI tools — ChatGPT, Claude, Gemini. It processes language and generates responses based on patterns learned from enormous amounts of text. The model is not the product. What's built on top of it is.
Prompt Engineering
The practice of crafting the instructions you give an AI to get better outputs. It matters — but it is the surface layer, not the foundation. A well-prompted bad setup is still a bad setup.
Knowledge Base
The body of information an AI is trained on or given access to. For a business AI agent, this is what makes it yours — your services, your voice, your answers. The quality of the knowledge base determines the quality of every response.
Guardrails
Defined limits on what an AI will and will not do. What it answers, what it escalates, what it never touches. Guardrails are not restrictions — they are what make an AI system trustworthy enough to represent your business.
Human-in-the-Loop
A design approach that keeps a human in the decision chain at the right moments. The AI handles what it can handle well. When it reaches its limit — or when the stakes are too high — a human steps in. Built in on purpose, not bolted on after a problem.
Automation
Using technology to perform a task without human intervention each time. Automation is not AI — a light on a timer is automation. But AI can power automation that adapts and responds rather than just executing a fixed sequence.
Hallucination
When an AI generates something that sounds confident and correct but is factually wrong. It is not lying — it is pattern-matching without knowing the difference. Guardrails and a well-scoped knowledge base are how you limit this in production systems.
RAG (Retrieval-Augmented Generation)
A method of giving an AI access to a specific body of information — your documents, your FAQs, your knowledge base — so its responses are grounded in your actual content rather than general training data.
Fine-Tuning
Training an AI model further on specific data to shape its behavior for a particular use case. Not always necessary — and often overkill for small business applications. RAG is usually the right starting point.
API
Application Programming Interface. A connection point that lets one piece of software talk to another. When your AI agent connects to your CRM, your calendar, or your website — it does that through APIs. You don't need to understand how they work, but you should know they exist.
AEO (Answer Engine Optimization)
Structuring your content so AI-powered search tools — ChatGPT, Perplexity, Google's AI Overviews — can accurately represent your business when someone asks a question relevant to what you do. The next layer beyond SEO.
Before You Build
These are not technical questions. They are business questions. If you can answer all of them clearly, you are ready to build. If you can't, that is where to start.
What is the one specific problem I want AI to solve?
Not AI in general. One problem. Lead qualification, after-hours responses, appointment routing — pick one. Businesses that try to solve everything at once end up solving nothing reliably.
What does success look like — specifically?
If you cannot define what winning looks like before you build, you cannot measure whether you won after. A number, a behavior, a time saved — something concrete.
What information does the AI need to do this job well?
Every AI system is only as good as what it knows. What services do you offer? What are your prices? What are the most common questions you get? What should it never say? This is your knowledge base — and it starts here.
Where does the AI stop and a human begin?
There are interactions that belong to you — conversations that require your judgment, your relationships, your authority. Know where that line is before anything goes live. This is your guardrail architecture.
What happens when it gets something wrong?
It will. Not often if it's built right — but eventually. What is the recovery process? Who finds out? How is it corrected? A business that hasn't thought through failure mode isn't ready to deploy.
Do I own what gets built?
Your knowledge base, your agent, your deployment — these should belong to you. Not to a vendor's platform, not to a subscription that disappears when you stop paying. Confirm ownership before you build anything.
Who maintains this after it goes live?
Your business changes. Your services, your pricing, your clients' questions — all of it evolves. An AI system that isn't maintained becomes outdated and then unreliable. Know who is responsible for keeping it current.
Ready to move from clarity to action?
Schedule a Consultation
Hello. I'm Soli — I know this business inside and out. What can I help you figure out today?