Helping account managers compare policies, verify coverage, identify gaps, and move faster inside their existing workflows

A growing insurance broker was looking to improve one of the most time-consuming parts of its client service operation: reviewing policy documents, checking renewals, comparing coverage changes, and responding to client and carrier questions with speed and accuracy.

The team managed a high volume of policies, endorsements, renewals, client emails, carrier requests, and supporting documents. Much of the work required experienced account managers to manually read long policy documents, compare versions, identify changed language, verify coverage, draft replies, and decide where each request should be routed.

The broker partnered with Notch to deploy internal AI co-pilot agents that support its service and account teams directly inside the tools they already use.

The challenge: policy work was manual, document-heavy, and hard to scale

Before Notch, policy checking and renewal review required significant manual effort. Account managers had to open multiple policy versions, review endorsements, compare coverage language, search for exclusions, verify limits, and confirm whether the latest document matched the expected client or carrier request.

This created several operational challenges.

Policy comparisons took too long. Even small language changes could be buried deep inside policy forms, endorsements, exclusions, or renewal documents. Teams had to manually search for modified language and decide whether the change was meaningful.

Coverage verification required deep context. A simple client question could require checking the policy, prior versions, endorsements, attachments, carrier emails, and internal notes before the team could respond confidently.

Renewal workflows created pressure. During renewal periods, account teams had to review policy changes quickly while still protecting clients from missed gaps, unexpected exclusions, or incorrect terms.

Email volume created repetitive work. Many inbound messages required the same steps: understand the request, locate the relevant policy context, identify missing information, draft a response, and route the issue to the right person.

Routing was inconsistent. Requests involving coverage questions, claims context, fraud signals, missing documents, or carrier follow-up often depended on manual judgment to determine the next step.

The broker needed more than a document search tool. It needed an internal AI co-pilot that could understand policy context, compare documents, surface decision support, draft replies, and help teams act faster without losing control.

The solution: internal co-pilot agents for policy checking and decision support

Notch deployed AI co-pilot agents to support the broker’s account managers, service team, and operations staff. These agents worked inside the broker’s existing workflows and helped the team review documents, answer questions, compare policies, identify gaps, and route work intelligently.

The policy checking agent compared policy versions with precise change tracking. Instead of asking account managers to manually scan two long documents, the agent highlighted modified language and allowed the team to jump directly to the sections that changed.

The broker also used a “talk to doc” experience, allowing team members to ask natural-language questions about a policy. For example, an account manager could ask whether a specific exposure was covered, whether a limit changed from the prior version, whether an exclusion was added, or whether a required endorsement was missing.

The agent did not just retrieve text. It provided source-linked answers, policy context, and decision support so the team could validate the response and understand where the answer came from.

Notch also helped generate email replies. When a broker received a client or carrier message, the co-pilot could identify the request, retrieve the relevant policy context, detect missing information, suggest next steps, and draft a response for the account manager to review and send.

Behind the scenes, Notch’s internal agents supported additional workflows such as coverage verification, claims decisioning support, fraud checks, smart routing, and gap identification. Requests were routed based on policy context, urgency, missing information, client priority, coverage question type, and operational workflow rules.

How the workflow worked

A client, carrier, or internal team member submitted a question, renewal document, policy version, endorsement, or email request.

The Notch co-pilot analyzed the relevant policies, attachments, prior versions, emails, and internal context.

For policy comparison, the agent identified modified language and highlighted what changed between versions.

For policy questions, the team could use “talk to doc” to ask natural-language questions and receive source-linked answers.

For email workflows, the agent summarized the request, identified gaps, pulled relevant policy context, and generated a draft reply.

For routing, the agent classified the request and sent it to the right person or workflow based on urgency, coverage topic, missing information, claims relevance, fraud signals, or client priority.

The account manager stayed in control, reviewing the AI output, validating the source-linked context, and deciding the final action.

The outcome: faster policy review, stronger client service, and amplified teams

With Notch, the broker gave its team an internal AI layer that made policy-heavy work faster, more consistent, and easier to manage.

Policy comparison became significantly faster because account managers could jump directly to modified language instead of manually reviewing entire documents. Renewal review became more efficient because the team could quickly identify changes, missing endorsements, unexpected exclusions, or coverage gaps.

Client and carrier responses also became faster. Instead of starting from a blank email, account managers received suggested replies grounded in policy context, supporting documents, and the specific question being asked.

The broker improved routing consistency by using AI agents to classify requests and surface the right next step. Coverage questions, claims-related issues, missing documentation, fraud indicators, and urgent client needs could be routed more intelligently instead of relying on manual triage alone.

Most importantly, the broker amplified its existing team. Notch did not replace the judgment of account managers or service teams. It gave them faster access to policy context, clearer document comparisons, better drafting support, and more reliable operational routing.

Core use case: Broker Co-Pilot for Policy Checking

Notch helps brokers turn policy documents, endorsements, renewals, emails, and attachments into actionable context for their teams.

The co-pilot compares policy versions, tracks modified language, answers questions about policy documents, identifies gaps, generates email replies, verifies coverage context, and routes work based on urgency and business rules.

The result is a faster, more accurate service workflow that helps account teams spend less time searching through documents and more time advising clients.

Bottom line

By deploying Notch’s broker co-pilot agents, the broker transformed policy checking from a manual, document-heavy process into an AI-assisted workflow built for speed, accuracy, and team leverage. Account managers could compare policy versions, jump directly to modified language, ask questions about coverage, identify gaps, generate client-ready replies, and route work intelligently from inside the tools they already use. The result was a more effective team, faster renewals, stronger client service, and measurable productivity gains across the broker’s policy and service operations.

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