AI advisory and implementation
AI services
What it is
Years working with owners, CEOs and deal teams shape how we approach AI: understand how the company makes money, then decide where technology belongs.
Employees stay in charge of the work. We build on what they know, then add agents for specific tasks under written rules, with a named person responsible.
AI advisory
We give an outside view before, during or after AI is put to use: where it fits, what to leave alone and which processes come first. We give a second opinion on tools and agents already in use.
The result is a written plan with priorities, the numbers to measure and next steps. You can start with questions alone.
AI implementation
We write down one process, agree on the goal, define the metric and record its starting point. Before an agent runs on live systems, we test it in a sandbox: a separate test setup built from past work or sample data, checked against cases your employees confirm as correct. Then we run a 90-day pilot under a one-page written rulebook. Access moves from read-only to draft-only to limited sending. A person approves anything that leaves the business, except routine items we name and agree on in writing.
In the first weeks we review the agents' work closely and tune it with your employees. If you choose, we then add more processes.
We train your people to manage and improve the agents, including what they should keep watching. Ongoing support is optional.
How we measure it
We review the metric at 30, 60 and 90 days against implementation and operating costs, and revise or stop before expanding.
- Quoting: time from request to quote
- Month-end close: days to close
- Scheduling: weekly hours spent
- Forecasting: time to update the forecast
- Owner's weekly numbers: hours to compile
Where we apply AI
We document each process first, then:
- Quoting and estimating: draft quotes from your pricing rules.
- Scheduling and production: flag conflicts and missing inputs.
- Shipping and logistics: prepare paperwork and status updates.
- Accounting and the close: organize records and flag missing items.
- Finance and forecasting: update working models and flag changes against plan.
- Marketing and the website: draft updates and route them for approval.
- The CEO's weekly numbers: gather figures and flag what needs attention.
Who owns it
The work runs in your accounts under agreed access rules. The prompts, workflows and documentation we write are yours to keep, on the terms in your engagement letter.
AI does not change what makes a business good
We believe strong businesses put customers first, do quality work and grow responsibly. AI does not change those priorities. We use it to reduce routine work and give people more time to serve customers.
Common questions
Where do we start?
We start with one process that regularly affects you or your customers. We write it down, agree on the metric and baseline, and run a 90-day pilot. After the first month, we review progress and decide whether to continue.
Can we implement AI ourselves?
Often, yes, for drafting, research and reports, and we will say when that is enough. Outside help is worth considering when the work touches financial records, customer data or payments, or must run daily without constant supervision.
How do we know the AI is right?
Before an agent touches live systems, we test it in a sandbox, a separate test setup built from past work or sample data, against a fixed set of examples your employees have confirmed as correct. We rerun those tests before we change an agent. Testing lowers the risk of errors but does not remove it, so we agree with you in writing on a standard, and work that falls outside it is routed to a person for review.
Do agents need ongoing monitoring?
Yes. In the first weeks we review the work closely, fix errors and adjust with your employees. After handover, the person you designate to manage the agents should keep watching their work. Ongoing support is optional.
What happens to our employees?
Your employees manage and train the agents, and we involve them from the start. Their knowledge guides the process steps, exceptions and improvements.
Is our data safe?
By default, the work runs in your own accounts. We explain where data goes, who can access it and what the AI provider retains. This does not remove every risk, so we agree on permitted data before building.
Do we have to clean up our data first?
Not necessarily. We start with the data the first process needs, identify its requirements and address only the gaps that would stop it from working.
What will it cost to run?
Before building, we document monthly operating costs, separating AI service, hosting and software expenses, so you can assess each.
Start with a conversation.
Tell us what your team spends its week on. No pitch and no pressure.