Lower running costs
Model and workload choices matched to the job, so routine work stops being billed at frontier prices.
365 Cloud Advisors
Sustainable AI
Better for your business. Better for our planet.
We design AI architectures that balance performance, cost and environmental impact. The goal is not maximal compute. It is the smallest amount of computing necessary to solve the business problem well.
This is for teams whose AI costs are climbing faster than the value, and who would rather engineer that down than accept it as the price of entry.
Most AI bills are inefficiency, not usage.
When an AI bill climbs, the instinct is to assume the system is being used more. Usually it is being used carelessly: the largest available model answering questions a much smaller one would handle, the same context re-sent on every call, whole documents retrieved where a paragraph would do, data moved between regions for no reason anyone can now recall.
None of that is visible from a summary invoice, which is why it persists. It shows up when you look at what each workload actually requires and compare it with what it has been given.
The convenient part is that efficiency and sustainability are the same engineering problem here. Compute that was never needed costs money and emits carbon in equal measure, and removing it improves both without a trade-off to argue about.
Model and workload selection
Token and API optimization
Efficient agent design
Data movement reduction
AI cost analysis
Sustainable cloud architecture
What you get
Deliverables, not a deck of slides.
A breakdown of where the spend goes
Cost attributed to workloads and use cases rather than arriving as one platform invoice, so the expensive thing is identifiable.
Model-to-task fit review
Which workloads are running on more capability than the task needs, and what the smaller option would cost and produce.
Prompt, context and retrieval efficiency
Where context is re-sent unnecessarily, where retrieval is pulling far more than the answer requires, and what caching would remove.
Architecture changes that cut waste
Data movement, region placement and orchestration choices that reduce cost and latency at the same time.
A measurement approach
How to track cost per outcome going forward, so efficiency does not quietly erode once the engagement ends.
Environmental impact context
The reduction expressed in energy terms as well as financial ones, for organizations reporting on it.
How it works
Where this sits in the route.
Step 1
Assess
Understand your business, systems, processes and AI readiness.
Step 2
Optimize
Improve performance, cost, accuracy and environmental impact.
Step 3
Automate
Deploy AI agents where they deliver measurable value.
Step 4
Govern
Implement responsible AI with security, permissions and human oversight.
Common questions
The things people actually ask.
Is a smaller model not just a worse model?
For a lot of tasks, no — classification, extraction, routing and summarising are frequently handled well by something far cheaper than the flagship. The judgement is task by task, which is why it is worth actually measuring rather than defaulting to the largest option.
Is this really about the environment, or about cost?
Both, and they do not conflict. Compute that was never necessary costs money and emits carbon in the same proportion, so the same engineering removes both. We would rather say that plainly than dress up a cost exercise as an environmental one.
Can you quantify the environmental impact?
We can express reductions in energy terms based on the workloads and regions involved. Precise carbon figures depend on the provider’s own reporting for the data centres you run in, so we are careful to present that as their number rather than ours.
Related reading
Written on this, at length.
AI Strategy
Microsoft Copilot vs. Custom AI: Which Is Right for Your Business Central Environment?
The answer is usually both — for different runs. The mistake is buying one pair of skis and expecting them to handle the whole mountain.
Data Architecture
Fabric, OneLake, Lakehouses and Data Lakes: What Do They Actually Have to Do With AI?
Everyone nods when somebody says “we should put all our data in the lake.” Almost nobody asks which lake, or what is already floating in it.
AI Architecture
Building an AI Integration Layer Around Business Central
Connecting an AI model to Business Central takes an afternoon. Connecting it correctly takes an architect — and you find out which one you did in the incident report.
Start here
Not sure this is the right run for you?
The Trailhead Assessment is a short set of questions about your systems, data and processes. It ends with a practical map of what to modernize, what to automate, and what to leave alone.