Small businesses ask us this question more than any other. The honest answer is that AI consulting cost depends less on "AI" and more on the plumbing around it: your data, your existing tools, and how private the system needs to be.
What actually drives the cost of an AI project?
Four factors account for most of the price difference between projects:
- Data readiness. If your documents are organized and current, setup is fast. If knowledge lives in scattered email threads and old PDFs, there is cleanup work first.
- Integrations. Every system the AI must read from or act in (your CRM, accounting tool, help desk) adds design and testing time.
- Privacy requirements. Using a hosted AI provider is the simplest option. Running a private model inside your own cloud account costs more to set up, but keeps every prompt and document in-house.
- Production readiness. A demo is cheap. A system with access controls, monitoring, cost tracking and a human approval step is what you actually want running your business, and it takes more care to build.
What should a small business build first?
Pick one workflow that wastes measurable time every week. The two most reliable first projects we see are:
- A knowledge assistant that answers staff or customer questions from your own policies, product documents or past tickets, with links back to the source.
- A single workflow automation, such as drafting replies to common requests for a person to approve before sending.
Both are low risk, quick to deliver, and easy to measure in hours saved.
How do you avoid paying for a pilot that goes nowhere?
Ask any consultant three questions before you sign:
- Where will this system run after the pilot, and who will maintain it?
- How will we measure whether it worked?
- What will it cost per month to operate?
If those answers are vague, the pilot is likely to stall. We plan the production setup during discovery, so the proof of concept is built on the same foundation the real system will use.
How long does it take?
A focused proof of concept typically takes two to six weeks. Larger rollouts are scoped after a one-week discovery phase, once we understand your data and integrations.
When does a private AI deployment make sense?
If you handle health records, financial data, legal documents or proprietary source code, sending that data to an outside AI provider may not be acceptable. In that case a private deployment is worth the extra setup. We delivered one for an engineering team that now uses a 120-billion-parameter model with no data leaving its environment. You can read the private AI case study.
The bottom line
Start narrow, measure honestly, and make sure the first project is built to survive contact with production. If you want a straight answer for your situation, book a discovery call or read more about our AI services.