AI cost optimization · AI where it pays
AI cost optimization: pay for the AI that does the job, and no more.
Cut what your AI costs to run. We right-size models, cache and route requests, and measure cost per process, for systems we build and for AI systems you already run. Book a free cost review and we will show you where the money goes.
Where AI running costs come from
- Using the largest model for every task, including simple ones
- Sending far more context than a task needs, on every request
- Repeating the same calls for the same answers
- Retries and loops that nobody is watching
- Infrastructure sized for peak load around the clock
What we do
- The right model for each task, with larger models kept for the steps that need them
- Caching of repeated requests and answers
- Routing, so simple requests go to cheaper paths
- Trimmed prompts and context, without losing quality
- Right-sized cloud infrastructure that scales with use
- Cost per process measured and reported, so savings are visible
How a cost review works
We look at your usage and bills, trace cost back to each process, and test cheaper options against the quality you need. You get a list of changes ranked by savings and risk. We can make the changes with your team or for you.
Quality first
Every change is checked against an evaluation of the system’s output, so cost comes down without the answers getting worse. If a cheaper option hurts quality, we do not recommend it.
Case studies
Questions
How much does AI automation cost, and how do you keep AI running costs low?
It depends on the process, so we quote a fixed scope after the audit rather than guessing up front. Most builds in our case studies took four to eight weeks. To keep running costs low we use the right model for each task, cache and route requests, and measure the cost per process, so you can see what each automated task costs to run.
Who owns the code and the data?
You do. We deploy into your cloud accounts, hand over the source code, prompts, and documentation, and you keep full ownership of the IP and the data.
What is a multi-agent system, and when do I need one?
Most processes need one well-built AI system. When a process has several distinct stages that need different skills, for example researching, drafting, checking, and approving, we use several specialised agents coordinated by an orchestrator. We use that design only when the process needs it, because simpler systems cost less to run and are easier to maintain.
Tell us where time goes today. We’ll show you what we’d automate first.
