$1BN Insurer Reports 5x Engineering Output With an Agentic SDLC

A specialty insurer's 30 person engineering org moved to an agentic SDLC on GitHub Enterprise, compressing feature delivery from 10 weeks to one and reporting roughly 5x throughput.

5x output

< 4 weeks

Implementation Time

Brian Benedict

Co-Founder & CCO

Eliza

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The Challenge

The insurer's 30 person engineering org was slowed by legacy tooling, inconsistent developer environments and workflows across teams, and AI that was adopted ad hoc rather than built in. The goal was to move to an AI native, agentic delivery model without disrupting governance.

What They Built

Eliza built an agentic SDLC operating model that embeds AI as an execution layer across the software lifecycle. It standardized developer environments, migrated to GitHub Enterprise, and unified branching, code review, and CI/CD conventions, with AI integrated into coding, debugging, testing, documentation, and peer review. The engagement was advisory and coaching led, and the client kept ownership of its infrastructure and code.

An advisory and coaching led engagement that standardized environments and conventions first, then embedded AI across the lifecycle, while the client retained code and infrastructure ownership. First value came in one month, over a three month engagement.

AI Role

AI acts as an execution layer across the software lifecycle, drafting code, tests, and documentation and assisting review, while engineers keep approval and governance.

Infrastructure

GitHub Enterprise; existing CI/CD

Integration Points

AI embedded across the GitHub Enterprise lifecycle, coding, debugging, testing, documentation, and peer review, on standardized environments and unified branching, review, and CI/CD

Impact

~5x output

Roughly 5x engineering throughput for the 30 person org after moving to an agentic SDLC

10 weeks to 1

Feature development cycle compressed from 10 weeks to one week

80-90% first pass

80 to 90 percent first pass completion rate on AI assisted work

Technology Utilized

Implementation Complexity

Time Savings

Best Fit For

Engineering leaders at mid sized, governed businesses such as insurers or financial services who want a step change in delivery speed without giving up control of their code or process.

Brian Benedict

Co-Founder & CCO

Eliza

Co-founder of Eliza, a boutique AI transformation firm and OpenAI Advanced Tier Partner. A two-time founder and former Hugging Face, he helps enterprises put AI to work through agentic engineering.

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Industry:

Insurance

Business Function:

Product & Engineering

Project Cost:

Cost not disclosed

AI Pattern:

AI-Accelerated Custom Software

AI Workforce Enablement

Value Type:

Time Savings

AI Model:

TBC

Frequently Asked Questions

How did an insurance company reach 5x engineering output with an agentic SDLC?

The experts embedded an agentic software development lifecycle on GitHub Enterprise, standardizing environments and unifying review and CI/CD, with AI across coding, testing, documentation, and review. The 30 person engineering org reported roughly 5x throughput.

What AI approach and tools were used?

The work built an agentic SDLC operating model on GitHub Enterprise, embedding AI as an execution layer across coding, debugging, testing, documentation, and peer review, delivered advisory and coaching led.

What results did the insurer achieve?

Feature delivery compressed from 10 weeks to one week, roughly 5x engineering throughput for the 30 person org, and an 80 to 90 percent first pass completion rate on AI assisted work.

How long did the engagement take?

First value came in one month, over a three month engagement.

Who is this agentic SDLC approach best for?

Engineering leaders at mid sized, governed businesses such as insurers or financial services who want faster delivery without giving up control of their code or process.

Originally published by

Eliza