AI Efficiency Audit and Optimization
An AI pilot that works doesn't always scale well: the same large model for everything, agents making too many calls, more context than needed on every request, or infrastructure sized for peak load instead of the average. Cost ends up growing faster than the value it creates. Our efficiency audit measures where that's happening in your operation and redesigns the architecture to sustain the same quality with fewer resources.
What this service covers
What an AI efficiency audit is
It's a technical and economic diagnostic of a system that's already in production: which models it uses, how much it consumes, how fast it responds, and what result it delivers for every dollar spent. It isn't an opinion about whether "AI is expensive" — it's a concrete measurement, comparing the current architecture against lower-cost alternatives that meet the same quality bar. It's essentially what's known as FinOps for AI (AI FinOps), applied not just to cloud spend but to the system's entire architecture.
What we measure
- Cost per task, per resolved ticket, per processed document, or per lead — not just the provider's total bill.
- Total cost of ownership (TCO): implementation, AI, cloud, licensing, support, and maintenance.
- P95 latency: the time under which 95% of executions complete, not just the average.
- Real automation rate: what share of cases gets resolved without human intervention.
- Quality, measured with whatever metric fits the process: accuracy, precision/recall, human acceptance rate.
What we usually find
- The same large model handling simple and complex tasks alike, with no routing criteria at all.
- Agents making more calls, retries, or reasoning steps than the task actually requires.
- More context than needed on every request: the whole document gets sent when a fragment would do.
- Infrastructure sized for peak traffic, idle most of the time.
- No one can answer what it costs, today, to resolve a ticket or process a document.
How we work
- Measure: we survey real consumption, current architecture, and cost per result.
- Attribute: we connect that consumption to the process and business area generating it.
- Compare: we evaluate alternatives (rule, small model, large model, agent) against the same quality bar.
- Redesign: we propose the lowest-cost combination that meets the requirement, prioritized by impact and effort.
- Implement: if Possition builds it, the optimization gets folded into the corresponding service.
- Monitor: periodic follow-up on unit cost, quality, and anomalies after the change.
Which architecture fits which task
There's no single answer: it depends on task complexity, the cost of an error, and how much latency the process can tolerate.
| Criterion | Rules / traditional software | Small model or classifier | Large model or agent |
|---|---|---|---|
| Fits when the logic is fixed and known | Yes | Sometimes | Not needed |
| Understands natural language or free text | No | Yes, for bounded tasks | Yes |
| Requires multi-step reasoning | No | Limited | Yes |
| Cost per task | Very low | Low | High |
| When it fits | Stable, deterministic process | Classify, extract, route, search | Open-ended writing or complex decisions |
Always pick the simplest option that meets the required quality — move up a column only when the previous one falls short, not by default.
Implementation process
- 1
Diagnostic
We analyze your process and tell you what to automate, how, and what return to expect.
- 2
Implementation
We build the solution connected to your systems, with working deliveries and real tests.
- 3
Operation and improvement
We leave it running, train your team, and measure results.
The full detail, with deliverables per stage, is in how we work.
Frequently asked questions
Does this replace the implementation we already have with Possition or another provider?
No. The audit evaluates what's already running, regardless of who built it. If something is worth redesigning, that implementation can be done by Possition or your own team — the diagnostic is independent of who executes afterward.
Do I need an obvious cost problem to request this?
Not necessarily. It's most useful when volume is growing and you still don't know what each result costs, or when "it works, but the bill is a surprise." The earlier it's measured, the cheaper it is to fix.
Do you promise a percentage of savings?
No. Savings depend on the current architecture, and we don't know it until we measure. Any figure offered before the audit would be a promise with no basis — we'd rather measure first and show the real number.
Is this the same as FinOps?
It overlaps partially. FinOps mainly looks at cloud spend; here we also look at the AI system's architecture itself — which model handles each task, how many calls an agent makes, how much context gets sent — which is usually where the biggest room for improvement is.
Does it apply if I use a single AI provider for everything?
That's actually the most common case we find: the same model handling simple and complex tasks alike. There's usually room for improvement without changing providers, just by changing which task goes to which model.
Already running AI in production and don't know what each result costs?
Tell us which system you'd like reviewed and we'll assess whether an efficiency audit makes sense before you keep scaling it.