TOOL SELECTION & AI COST
Pick the right AI. Know what it costs.
Most AI programs stall on the same three questions: which tool, at what cost, and what did we get for it. We help you answer all three with numbers.
What we hear
“We’re paying for five AI tools that do roughly the same thing.”
“Our AI bill doubled, and nobody can say which team or use case drove it.”
“We picked a vendor from a demo, and it doesn’t hold up on our own data.”
“Leadership wants to know what AI has delivered, and we can’t show it.”
Tool selection
We start from the work you need done, not from the vendor list.
- Use case first: define the task, the volume, the accuracy you need, and the risk if it’s wrong.
- The right type of model for the job: a large LLM model, a smaller LLM model, or a traditional ML model where it’s cheaper and more reliable.
- Build, buy, or extend what you already license.
- Vendor due diligence: how your data is used and retained, security, and the governance features you’ll need later.
- A scored pilot on your own data: accuracy, cost per task, and risk, side by side.
You get a shortlist, a scorecard, and a clear recommendation.
AI cost management (AI FinOps)
Token prices tell you what AI costs to run. They don’t tell you what a finished piece of work costs, or whether it was worth it.
- Cost per task and per outcome, not just cost per token.
- Spend you can see by team and by use case.
- Guardrails: budgets and alerts, routing simple work to smaller LLM models, reusing results for repeated work, and right-sized prompts.
- Value tracking: the business result each use case is meant to deliver, measured monthly next to its cost.
You get a cost baseline, a unit-cost model, guardrails, and a monthly cost-and-value report.
Why it belongs with governance
The controls that keep AI safe are the same ones that keep it affordable: knowing what runs, where, on what data, and for whom. We put cost and risk controls in the same place, at the point where AI runs, so one inventory and one set of records serve both.
Start with the assessment.
In about four weeks, you’ll know what AI you’re running, where you stand on the maturity curve, and what to fix first.