AI agents and RAG,
in plain English.

The terms, without the hype

LLM (large language model). The engine behind tools like ChatGPT and Claude. It reads text and writes text. It is very good at summarizing, drafting, sorting, and pulling details out of messy writing. It does not "know" your business, and it can state wrong things with confidence.

RAG (retrieval-augmented generation). A way to make an LLM answer from your documents instead of from memory. When someone asks a question, the system first finds the most relevant passages in your files, then asks the model to answer using only those passages. A good RAG assistant shows citations, so you can click through and check the source.

AI agent, or agentic workflow. An LLM that can take steps, not just write answers. It can use tools you allow, like searching your inbox, looking up a customer, or creating a draft record. The key word is "allow." A well-built agent works inside clear limits: which tools it can touch, what it can change, and when it must stop and ask a person.

How this differs from ordinary automation. Tools like Zapier, Make, or Power Automate follow rules you write: "when a form is submitted, add a row and email Sam." They do exactly the same thing every time. AI is useful when the input is messy, like free-form emails or scanned forms, and a fixed rule can't handle it. If a rule can do the job, a rule is usually cheaper and more predictable.

Side-by-side comparison

What this looks like in a small business

These are hypothetical examples, meant to show the shape of a good first project.

An internal knowledge assistant

For example, a property management office might have SOPs, lease policies, and vendor instructions spread across shared drives. A RAG assistant lets staff ask plain questions and get answers with links to the exact document. New hires stop interrupting the one person who knows everything. When the assistant can't find a good source, it should say so instead of guessing.

An email triage agent

For example, a home services company might get a steady stream of quote requests, scheduling questions, and complaints in one inbox. An agent could read each message, label it, draft a reply, and prepare a new lead record in the CRM. A person reviews the draft and the record, then clicks approve. Nothing goes to a customer without that click.

Document extraction

For example, a small accounting practice might receive invoices and intake forms as PDFs and photos. An LLM can pull out names, dates, amounts, and account numbers into a structured form. A person checks the fields against the original before the data goes into the books.

What makes AI reliable enough to use

LLMs make mistakes. That's not a reason to avoid them. It's a reason to build around them. These are the safeguards that matter most:

• Grounding and citations. Answers should come from your sources, and show them. If there's no good source, the system should say "I don't know."
• Testing on real examples before launch. Run the system on a set of past emails, questions, or documents where you already know the right answer. Look at every miss.
• Human review checkpoints. Decide which steps need approval. Anything sent to a customer, moving money, or changing records should start with review.
• Least access. Give the AI only the tools and data it needs. Read-only where possible. Its own login, not someone's admin account.
• Logging. Keep a record of what the system saw, what it did, and who approved it, so you can trace any mistake.
• Care with personal and financial data. Know which AI vendor processes your data, what their data-use terms say, and whether your industry has rules. Keep sensitive fields out of steps that don't need them.
• Cost monitoring. Most AI models are priced by usage, roughly by how much text goes in and out. Costs are often modest, but they grow with volume and with long documents. Set budgets and alerts.

Sometimes the honest answer is that you don't need AI at all. If the input is already clean and the steps never change, a plain automation will be cheaper, faster, and easier to trust.

How to start

1. Pick one workflow. Look for something high-volume and low-risk, where a mistake is easy to catch and fix.
2. Gather 20 to 50 real examples. Real emails, real questions, real documents, including the awkward ones.
3. Define what "good" looks like. Write down the right answer or output for each example. This becomes your test.
4. Pilot with review. Run it alongside your team, with a person approving every output, for a few weeks.
5. Measure. Track how often outputs are approved as-is, edited, or rejected, and what it costs to run. Loosen review only where the results earn it.

Where Bootable fits

Bootable builds agentic workflows, RAG knowledge assistants, and LLM integrations with guardrails and human review. Here's how the offers line up. All prices are on the pricing page.

• Free 20-minute project call. Talk through your idea and whether AI is the right tool for it.
• AI strategy session, $400 fixed. 90 minutes plus prep and written next steps. Good if you want a clear plan before spending more.
• Starter AI automation, $3,500 fixed. One workflow using tools you already have, delivered in about two weeks.
• AI opportunity audit, $2,500 fixed. A structured look at where AI fits in your operations. Half is credited toward a build within 60 days.
• Custom AI builds from $7,500. Connected workflows typically run $12,000 to $20,000. Custom systems typically run $20,000 to $35,000. These are planning ranges, not quotes.
• Ongoing care from $350/month, and advisory work at $200/hour.

AI model, API, and software costs are billed at cost.