Workload Intelligence for Insurance: Bring Your Prompts, See What Fits
Submit production prompts, get a data-driven model selection report
Pnyx capability: Workload intelligence — analyzing production prompts and returning a data-driven model selection report. No routing required. Submit prompts, and Pnyx scores the skills each one demands, maps current model assignments against what's needed, and shows where the gaps are.
Bring Your Prompts. Get Answers.
Most carriers pick AI models the same way: a team tests two or three options during a pilot, picks the one that seemed to work, and deploys it. The choice sticks — rarely revisited, never evaluated against the full workload.
Pnyx works differently. A carrier submits its production prompts and Pnyx analyzes every one of them — scoring the skills each prompt demands, and mapping that against what available models actually deliver.
The output is a workload intelligence report: which models fit which prompts, what percentage of the workload goes to each capability tier, and where the current assignments are costing more than they should or delivering less than the task requires.
No integration. No code change. Just data — so the carrier can make model selection decisions with evidence instead of assumptions.
Why Insurance
Insurance runs a wide spread of AI tasks under one roof. A P&C carrier might use AI for claims intake, photo damage assessment, fraud detection, underwriting evaluation, and policyholder communications — all at production scale. Each task has a different complexity profile, but most carriers assign models without measuring that difference.
Two things make this especially consequential for insurance:
Regulators want to see the rationale. The NAIC's Model Bulletin, adopted by 23 states, requires carriers to document how they select and govern AI systems. A workload analysis gives carriers that documentation — built from data, not assertions.
Under-provisioning has outsized cost. A model that's too weak for fraud detection misses signals. A model that's too weak for underwriting produces findings that don't hold up. In insurance, the cost of using the wrong model on high-stakes work isn't just quality — it's financial exposure.
What the Carrier Gets
A regional P&C carrier exports its production prompts — anonymized, no policyholder data — and submits them to Pnyx.
Pnyx evaluates every prompt across capability dimensions — reasoning depth, domain knowledge, language quality, safety sensitivity, instruction complexity — and clusters them by workflow.
The report shows three things:
How the workload breaks down
| Workflow | Volume | Complexity | What it needs most |
|---|---|---|---|
| FNOL intake | High | Low | Speed and structured extraction |
| Photo damage assessment | High | Medium | Vision capability and throughput |
| Claims triage | Medium | Medium | Moderate reasoning |
| Fraud detection | Low | High | Deep analytical reasoning, safety awareness |
| Underwriting evaluation | Low | Very High | Frontier reasoning, domain expertise, instruction fidelity |
| Policyholder comms | High | Low | Language quality and tone control |
Most carriers discover that the majority of their prompts are low-to-medium complexity — work that efficient models handle at full quality. A smaller percentage requires frontier-level capability. Knowing the ratio changes everything about model selection.
Where current models fit — and where they don't
| Workflow | Current model | Fit |
|---|---|---|
| FNOL intake | GPT-5 | Over-provisioned — a model three tiers lower performs identically |
| Photo damage assessment | Gemini 2.0 Flash | Well-matched |
| Claims triage | GPT-5 Mini | Appropriate |
| Fraud detection | GPT-5 Mini | Under-provisioned — task demands more reasoning than this model delivers |
| Underwriting evaluation | Claude Sonnet 4.5 | Under-provisioned — most demanding task on a mid-tier model |
| Policyholder comms | GPT-5 | Over-provisioned — paying for reasoning on a language quality task |
Where the money is going
The carrier sees that its highest-volume, lowest-complexity workflows account for the majority of inference spend — because they're running on models priced for capabilities they don't use. Meanwhile, the highest-stakes workflows are on cheaper models that lack what the task demands.
The spend profile is inverted. The data makes it visible.
Why This Requires a Neutral Layer
Every model provider can tell you what their models are good at. None of them will tell you that a competitor's model is a better fit for half your workload — or that their own flagship is over-provisioned for your most common tasks.
Pnyx is provider-agnostic. The analysis is driven by what the prompts require, not by what any single provider sells. The carrier gets a clear picture across the full model landscape — OpenAI, Anthropic, Google, open-source — scored against their actual production work.
The Adoption Path
Submit prompts. Export production prompts from each workflow, anonymized. Pnyx needs the structure and complexity, not the underlying data.
Receive the report. Workload breakdown, model fit analysis, and spend mapping — all built from the carrier's own prompts.
Make decisions with data. The report is the carrier's property. Use it to select models, renegotiate contracts, document governance for regulators, or make the internal case for changing assignments.
Optional: activate routing. If the data makes the case, Pnyx can implement the recommendations as automated routing. But the analysis stands on its own.
See how Pnyx routes your workloads
Try the Prompt Analyzer or request early access to the routing gateway.