Client
National Insurer
Challenge
Manual intake and review of policy questions, handwritten forms, and FNOL reports consumed tens of thousands of hours weekly, slowed response times, and introduced inconsistency and compliance risk.
Solution
StackAI deployed AI agents for policy Q&A, handwritten form processing, and FNOL triage, automating intake, ensuring consistency, and saving 25,000–30,000 staff hours every week.
Overview
A national commercial insurer partnered with StackAI to modernize its most time-consuming operations. From handwritten forms and emailed loss reports to messy policy documents, the insurer faced bottlenecks that drained staff time and slowed customer response. By deploying AI-powered agents to handle policy Q&A, handwritten form processing, and FNOL triage, the firm transformed its workflows into fast, scalable, and auditable systems that save tens of thousands of hours every week. The results?
Tens of Thousands of Hours Saved: By automating policy Q&A, handwritten form transcription, and FNOL triage, the insurer now saves an estimated 25,000–30,000 staff hours every week that were once spent on manual intake and review.
Faster and More Reliable Decisions: FNOL acknowledgments are sent instantly instead of within hours or days. Policy questions that previously tied up support staff are answered in seconds, always citing the correct clause. Accuracy has improved by over 90% compared to manual data entry on handwritten forms.
Scalable Without New Hires: The company can now handle intake volume growth of 2–3x without adding staff, eliminating the bottlenecks that previously slowed operations.
Audit-Ready Outputs: Every workflow generates structured outputs (clean JSON or formatted docs) that can be searched, logged, and reviewed. This creates a transparent audit trail for compliance while unlocking downstream analytics.
With these changes, AI agents are no longer a mere demo, but the backbone of daily operations, cutting costs, improving oversight, and modernizing customer experience.
From Policy Text to Clear Answers
The Problem: Opaque and Confusing Policy Documents
Policyholders and internal teams often struggled to interpret lengthy insurance policy documents. Staff were fielding repetitive questions about coverage, exclusions, and limits. Worse, answers could vary depending on who was asked, creating inconsistency and compliance risk.
The Solution: AI-Powered Policy Q&A Chatbot
The insurer deployed a StackAI chatbot trained strictly on its own policy documents. When a user asks a question, the chatbot searches the policy library, retrieves relevant sections, and generates a clear answer citing exact clauses. If the policy text doesn’t provide a clear answer, the chatbot explicitly says so instead of speculating. The result is consistent, policy-based answers for both staff and customers, without legal ambiguity or wasted hours.
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Client
National Insurer
Challenge
Manual intake and review of policy questions, handwritten forms, and FNOL reports consumed tens of thousands of hours weekly, slowed response times, and introduced inconsistency and compliance risk.
Solution
StackAI deployed AI agents for policy Q&A, handwritten form processing, and FNOL triage, automating intake, ensuring consistency, and saving 25,000–30,000 staff hours every week.
Overview
A national commercial insurer partnered with StackAI to modernize its most time-consuming operations. From handwritten forms and emailed loss reports to messy policy documents, the insurer faced bottlenecks that drained staff time and slowed customer response. By deploying AI-powered agents to handle policy Q&A, handwritten form processing, and FNOL triage, the firm transformed its workflows into fast, scalable, and auditable systems that save tens of thousands of hours every week. The results?
Tens of Thousands of Hours Saved: By automating policy Q&A, handwritten form transcription, and FNOL triage, the insurer now saves an estimated 25,000–30,000 staff hours every week that were once spent on manual intake and review.
Faster and More Reliable Decisions: FNOL acknowledgments are sent instantly instead of within hours or days. Policy questions that previously tied up support staff are answered in seconds, always citing the correct clause. Accuracy has improved by over 90% compared to manual data entry on handwritten forms.
Scalable Without New Hires: The company can now handle intake volume growth of 2–3x without adding staff, eliminating the bottlenecks that previously slowed operations.
Audit-Ready Outputs: Every workflow generates structured outputs (clean JSON or formatted docs) that can be searched, logged, and reviewed. This creates a transparent audit trail for compliance while unlocking downstream analytics.
With these changes, AI agents are no longer a mere demo, but the backbone of daily operations, cutting costs, improving oversight, and modernizing customer experience.
From Policy Text to Clear Answers
The Problem: Opaque and Confusing Policy Documents
Policyholders and internal teams often struggled to interpret lengthy insurance policy documents. Staff were fielding repetitive questions about coverage, exclusions, and limits. Worse, answers could vary depending on who was asked, creating inconsistency and compliance risk.
The Solution: AI-Powered Policy Q&A Chatbot
The insurer deployed a StackAI chatbot trained strictly on its own policy documents. When a user asks a question, the chatbot searches the policy library, retrieves relevant sections, and generates a clear answer citing exact clauses. If the policy text doesn’t provide a clear answer, the chatbot explicitly says so instead of speculating. The result is consistent, policy-based answers for both staff and customers, without legal ambiguity or wasted hours.
Turning Handwritten Documents into Usable Data
The Problem: Manual Data Entry From Handwritten Forms
Thousands of handwritten claim forms (First Report of Injury, Property Loss Notices, and General Liability reports) arrived each month via fax, scan, or photo. Staff had to manually transcribe these forms into structured fields, a slow and error-prone process that consumed massive amounts of time and delayed claim setup.
The Solution: Automated Handwritten Form Agent
With StackAI, the insurer now allows staff or policyholders to upload handwritten forms directly. The agent runs OCR to extract the text, then uses an LLM to identify structured fields such as claimant name, policy number, date of loss, and incident description. A clean Markdown summary is generated for adjusters, with missing or illegible fields flagged for review. The final summary is automatically written to a Google Doc, linked to the claimant’s file for easy access. This shift removed thousands of hours of manual data entry every week.
“Before the chatbot, the team was constantly bogged down fielding the same questions and trying to pull up dense policy text on the fly. Answers weren’t always consistent, and that created real risk and customer confusion. Now, with StackAI, everyone gets the same clear, clause-based response in seconds. The consistency and time saved have been a huge relief.”
Hakan Gureren
Go to Market
Turning Handwritten Documents into Usable Data
The Problem: Manual Data Entry From Handwritten Forms
Thousands of handwritten claim forms (First Report of Injury, Property Loss Notices, and General Liability reports) arrived each month via fax, scan, or photo. Staff had to manually transcribe these forms into structured fields, a slow and error-prone process that consumed massive amounts of time and delayed claim setup.
The Solution: Automated Handwritten Form Agent
With StackAI, the insurer now allows staff or policyholders to upload handwritten forms directly. The agent runs OCR to extract the text, then uses an LLM to identify structured fields such as claimant name, policy number, date of loss, and incident description. A clean Markdown summary is generated for adjusters, with missing or illegible fields flagged for review. The final summary is automatically written to a Google Doc, linked to the claimant’s file for easy access. This shift removed thousands of hours of manual data entry every week.
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