A cyber submission comes in. It is a half-filled application with blank PII record counts, plus an email that says "please quote." One broker, eager to get to market quickly, makes some assumptions and sends it out. Another broker, who has lost too many deals while firming up details on the backend, writes back and asks for confirmation on the account details.
This is the art of placement. It is a series of small, precise decisions made at hundreds of points in the process that determine how the risk is perceived and how the deal gets done.
In our last post, How Brokerages Scale Strategic Placement, we covered how placement can bottleneck your brokerage or accelerate growth. Agentic workflows are rapidly reshaping how brokerages differentiate. Today, we are digging into what separates commoditized LLM capabilities from personalized AI built to help brokerages scale placement.
If there is not always a single right answer in placement, how should insurance professionals think about the accuracy of the AI they deploy?
In most industries, accuracy means matching an objective truth. In insurance, the "correct" way to present data for a risk depends on experience, carrier relationships, strategy, and the way each team and individual prefer to work. Accuracy in this context includes extracting fields correctly and representing data in a way that aligns with how you think about risk.
As foundation models improve, raw capability will commoditize and everyone will have access to powerful document intelligence. Your competitive advantage remains your differentiated placement strategy, now embedded in your AI.
Firms need to capture the efficiency gains of automation while preserving what makes them different.
At Herald, we believe insurance firms should be able to capture AI advances while preserving the way they place business. We separate the intelligence layer from the strategy layer to deliver Personalized AI. With Herald, firms can trust two things:
Strategy control means ownership of the decisions that govern how submissions move through the firm. In placement, those decisions show up in three places:
We help customers configure these layers directly, while we maintain and improve the underlying intelligence. The result is AI that evolves with the technology and preserves how your team places business.
Triage is where placement strategy first becomes operational.
When a submission arrives, there are immediate decisions to make. Is this within appetite? Is it complete enough to move forward? Should my team handle it? Those decisions are not universal. They vary by firm, line of business, capacity, and distribution strategy.
At Herald, we work with brokerages to capture judgment directly into the AI agent context, tools, and prompts. Customers have embedded rules such as:
Triage is where firms encode how they allocate time, leverage distribution channels, and protect team focus before a person even sees the submission.
Extraction is where risk interpretation becomes explicit. When a submission says "PII between 50k-100k" or "MFA rollout in progress," there is rarely one universal output. One firm may assume the higher bound. Another may pause for clarification. A third may reference prior submissions or internal notes before proceeding.
These are strategic choices. At Herald, customers define extraction behavior at the field level. They configure:
The underlying models read documents. The way information is structured, validated, and advanced reflects your team's standards. Every field is benchmarked against labeled examples, so accuracy is measured according to your firm's definition.
Once a submission has been triaged and interpreted according to your rules, it can be advanced accordingly. At this stage, broker judgment translates into execution. Should the risk be sent to digital markets or handled directly through carrier relationships? Should a quote request be initiated? Does the submission need repackaging before going out? Who should be notified internally?
In Herald, customers define this execution logic in their strategy layer. Based on configured rules, agents can:
The autonomy operates within boundaries your firm defines. When triage and extraction reflect your standards, actioning reflects those standards as well. The result is a system that advances submissions according to how your team takes business to market.
Our customers run agentic workforces that operate like seasoned insurance professionals.
Insurance will run on AI. The firms that win will run on AI that thinks about risk the way they do.
