AI for Small Business
Agency rate cards start at $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.
Why It Feels Out of Reach
Most AI marketing is written for companies that already keep a data team on staff and a budget line to match, so "AI integration" reads like a project you could not staff if you wanted to. In McKinsey's State of AI, fielded in mid 2025, 88% of respondents said their organization regularly uses AI in at least one business function. That sample spans a range of company sizes and the survey weights its answers by none of them, so what it measures is not the small business down the road.
Spend turns out to be a poor predictor of whether an AI project pays for itself. The ones that return something start from a single named bottleneck and stay scoped to it.
At your scale, AI integration means putting a model inside a process someone on your team runs by hand today: answering the same questions, sorting incoming requests, finding the document that answered this exact question four months ago.
What AI integration looks like for small businessWhere It Pays Off
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. Someone types the question the way they would say it out loud and gets the answer back with its source attached, rather than paging through a shared drive for something the company already wrote down.
A customer can search your catalog by describing what they need instead of guessing at your product names, an intake form can fill itself from a few sentences of free text, and a quote estimator can price a job from what similar jobs actually cost you. Each one is an addition to the product you already run, not a rebuild.
A model earns its place when the work needs a call rather than a rule: sorting free-text messages into categories, pulling fields out of paperwork that arrives in ten different layouts, routing by meaning instead of a keyword. 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
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.
Published agency rate cards for enterprise AI work start around $60,000, because that work is priced for enterprise procurement and Fortune 500 infrastructure. Scope the same kind of work for one small business (one model, your data, an interface your team will actually open, deployment) and it starts in the thousands, not the tens of thousands. Running costs for a typical internal tool land under what you already pay for one mid-tier SaaS seat. The line item that moves the total most is the decision about what to build: a clear problem with defined data moves fast, while "add AI to our business" stalls.
Are You Ready?
Something the team does over and over that comes down to reading, sorting, or writing the same kind of information. Name it in a sentence and we can scope it.
Tidy is not a requirement. If the information sits in a system we can reach, RAG reads it at query time, so there is no warehouse to build first.
A task a new hire could learn from a five-minute walkthrough is usually a task a model can be pointed at. Judgment that lives only in one person's head is the part that stays manual.
A named outcome, a scope, and an agreed definition of done. Open-ended research programs are a different kind of spend, and we would say so.
Three out of four is a project we can scope today. If the gap is the problem statement itself, the first piece of work is choosing the problem rather than choosing a model.
Most of that time goes to the work around the model: getting access to your systems, handling auth, shaping the data, and building a screen someone will open twice a day. A scope that arrives clear is what keeps these ranges honest.
FAQ
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.
No. A database, a CMS, a shared drive of documents, or a set of maintained spreadsheets is enough to begin. RAG reads what you already have at query time, so there is no warehouse to build and no migration to sit through first.
For a low-volume internal tool, the model bill usually lands under what one mid-tier SaaS seat costs you. Anything customer-facing rises with traffic, but it tracks usage rather than jumping in steps. We set cost caps and monitoring at launch so there are no surprise invoices.
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.
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.
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.