The operating standard for enterprise ai
Not every AI task needs the biggest model
A framework for teams evaluating model strategy, cost, latency, data exposure, and governance before scaling AI across customer-facing workflows.
Evaluating model fit, cost, latency, and data exposure at scale
Customer-facing AI systems must be evaluated across four core dimensions: model fit by task, cost to serve at volume, latency in live workflows, and data exposure during inference. Decisions made at this layer directly impact performance, economics, and risk once AI is deployed across millions of customer interactions.
The report explains how enterprise teams can evaluate model fit, routing, governance, and infrastructure before scaling AI across customer-facing workflows.
When to use specialized models vs. frontier LLMs
How model choice affects cost, latency, and data exposure
What governance needs to exist around AI outputs
How to evaluate vendors beyond demo performance
Why production AI requires routing, evals, infrastructure, and human oversight
What questions to ask before scaling AI into customer-facing workflows
A framework for evaluating enterprise AI architecture
The report breaks down four levels of AI architecture, from wrappers to full-stack ownership, and what each means for cost, latency, privacy, and control.
Wrappers
Fast to launch. Limited control.
Harnesses
Better routing and workflow logic. Still dependent on external models.
Specialized models
Purpose-built for domain tasks with stronger cost and latency profiles.
Full-stack ownership
Models, data, routing, governance, workflow, and infrastructure controlled together.
Customer-facing AI touches sensitive data by default
Customer conversations often contain account details, addresses, payment information, health context, policy numbers, complaints, refunds, and other sensitive information.
The report explains why data boundaries, redaction, and inference architecture need to be evaluated before production deployment.
29%
of customer conversations contain sensitive personal information
47%
in financial services and insurance
full-stack control vs. third party llm
30x
lower cost to serve
3.5x
higher throughput
4x
lower latency