AI-Powered Automation Prototype Reduces Carrier Onboarding Time by 95%

At a glance

Leading insurance provider
Insurance
AI-powered automation prototype built during an Innovation Sprint by the Gorilla Logic Construct™ team
GPT-powered models, PostgreSQL, MinIO, containerized microservices
The Challenge

As part of a larger Carrier State Acceleration initiative, a leading insurance provider sought to streamline its slow, manual carrier onboarding process. Each new integration required weeks of back-and-forth between internal teams and carriers to gather, validate, and structure documentation.

The company faced several key challenges:

  • Product Management Team (PMT) bottleneck: a single PMT served as a bottleneck, limiting the number of carriers that could be onboarded in parallel.
  • No authority to enforce documentation standards: every carrier delivered information in different formats, from incomplete PDFs to unstructured spreadsheets.
  • Manual data entry and Excel comparisons: led to delays, errors, and an unsustainable workload.

To accelerate growth and improve efficiency, the client partnered with Gorilla Logic’s Construct™ team to explore whether AI could automate and standardize the documentation process.

The Solution

During an Innovation Sprint, Gorilla Logic’s Construct™ engineers designed and validated a containerized, AI-powered prototype that automated the most time-consuming phases of carrier onboarding.

Using the team’s AI-first development methodology, the proof of concept was built and tested in just 10 hours and featured:

  • EDEC 2.0 Framework: a standardized schema developed and validated with representative carrier data to unify documentation formats and support structured querying.
  • Automated Documentation Extraction: GPT-powered models parsed PDFs, spreadsheets, and Word documents into standardized data outputs.
  • Human-in-the-Loop Validation UI: allowed engineers to review, refine, and approve extracted fields quickly and confidently.
  • Schema Comparison and Spec Generation: compared extracted outputs against internal schemas to highlight gaps and generate integration-ready specifications.
  • Containerized Architecture: built on scalable, secure microservices using PostgreSQL and MinIO for rapid deployment and validation.

The Results

The prototype demonstrated how AI-powered automation can dramatically accelerate carrier integration without sacrificing accuracy or control:

  • 95% Faster Processing: reduced requirements documentation from 1–3 weeks to minutes in a controlled test environment.
  • Standardized Carrier Profiles: produced structured, reusable data models that can be queried and modified to support future integrations.
  • PMT Efficiency Gains: shifted the PMT’s focus from manual data entry to validation and strategic decision-making, improving both quality and throughput.
  • Proven Feasibility: the prototype validated measurable business impact and scalability potential, positioning the client to move toward enterprise integration.