AI for Small Business

AI for Small Business, priced like it.

Enterprise agencies quote $60,000. Your problem rarely costs that much to solve.

Most small businesses need one focused integration: a model connected to your own data, working inside a process you already run. You see working software in weeks, not months.

Tell us what you want to automate
Why It Feels Out of Reach

The AI conversation was built for enterprises.

The conversation around AI has been shaped by companies selling six-figure engagements, so "AI integration" sounds like something that needs a dedicated team and a dedicated budget. 88% of organizations now use AI in at least one business function, but most of that adoption sits with large companies that have both.

The businesses that get real value from AI aren't the ones spending the most. They start with one specific bottleneck and scope a focused project around it.

At your scale, AI integration isn't a chatbot bolted onto your homepage. It connects a model to your specific data and embeds it in a process someone on your team does by hand today: answering the same questions, sorting incoming requests, searching for information you already wrote down somewhere.

What AI integration actually looks like for a small business
Where It Pays Off

Three use cases that work at your scale.

Internal tools that know your data

We use RAG (retrieval-augmented generation) to connect a model to your own documents, so it answers from your SOPs, catalog, or help content instead of guessing. Your team asks a question in plain language and gets a sourced answer in seconds, instead of digging through shared drives.

Customer-facing features

Natural-language search on your product catalog. An intake form that pre-fills from what the customer described. A quote estimator grounded in past project data. Small additions that make an existing product smarter, built on top of what you already run.

Workflow automation

When the task involves judgment (classifying unstructured text, reading documents that don't follow a fixed format, routing by meaning rather than a keyword), a model earns its place. When the workflow is "when X happens, do Y," we build it with rules and save you the cost of a model you don't need.

Build, Buy, and What It Costs

Custom isn't the same as expensive.

Before building anything custom, check whether an off-the-shelf tool already solves it. A tool that handles 80% of the problem today usually beats a custom build that handles 100% in two months.

Custom is worth it when the AI needs to connect to your existing systems, when the off-the-shelf option requires the manual data entry you're trying to eliminate, when per-seat pricing multiplies as you grow, or when your data can't leave your infrastructure. For most SMB projects, custom means one model, one or two data sources, and a focused interface.

Enterprise AI agencies start around $60,000 because their pricing reflects enterprise sales cycles and Fortune 500 infrastructure. A scoped SMB project (connecting a model to your data, building the interface, deploying it) starts in the thousands, not the tens of thousands. The ongoing model API usage runs less than a typical SaaS subscription for most internal tools. The biggest cost is usually deciding what to build: a clear problem with defined data moves fast, while "add AI to our business" stalls.

When to build custom vs. use off-the-shelf
Are You Ready?

You need four things, not a data science team.

1

A clear problem

A specific task your team repeats today: answering the same questions, classifying inputs, searching documents, or generating routine output.

2

Reasonably organized data

It doesn't have to be perfect. A database, a CMS, organized files, or well-structured spreadsheets is enough to start. RAG retrieves from data that exists.

3

A describable process

If you can walk someone through the steps, a model can probably handle them. The clearer the process, the more reliably the AI follows it.

4

Budget for a focused project

A defined deliverable with a scope and a definition of success, not an open-ended R&D initiative.

If the only piece missing is the clear problem, that's where to start, not the technology.

What a realistic timeline looks like

RAG-powered internal tool (one data source, conversational interface) 4 to 6 weeks
Customer-facing AI feature (integrated with your product) 6 to 8 weeks
Multi-system workflow automation 6 to 10 weeks
Voice AI assistant with real-time interaction 8 to 12 weeks

The variable usually isn't the model. It's the integration work: connecting to your systems, handling auth, formatting data, and building an interface people want to use.

FAQ

Frequently Asked Questions

Will AI actually help my business, or is it hype?
It helps when there's a specific, repetitive task involving information processing and your data is accessible. It doesn't help when the problem is vague or a human needs to make every call. We tell you which one you have before quoting anything.
Do I need a lot of clean data?
No. If your information lives in a database, CMS, organized files, or structured spreadsheets, that's enough to start. RAG retrieves from data that exists. It doesn't require a data warehouse.
What will it cost to run after launch?
For most internal tools, model API usage runs less than a typical SaaS subscription. Customer-facing features with high traffic cost more but scale predictably. We set up cost monitoring so there are no surprise invoices.
What if a better AI model comes out later?
We build model-agnostic, so swapping the underlying model is a configuration change rather than a rewrite. That also keeps you out of one provider's pricing and lock-in.
How do we know the AI is reliable enough to ship?
A working demo doesn't prove a feature is production-ready. We build in evals, fallbacks, cost caps, and guardrails so the feature holds up after launch, not just in testing.

Tell us what you're trying to automate.

Describe the task that's eating your team's time. We respond within one business day with whether AI is the right tool, and a rough scope if it is.

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