5x Engineering Output With Agentic SDLC at a $1BN Insurance Company

Client:

Speciality Insurance Provider

Time to First Value:

1 month, 3 months total engagement

Users Enabled:

Our Problem

Problem

A $1B insurance company was working with legacy source control, inconsistent developer environments, and uneven AI adoption. Delivery was slowed by fragmented workflows, manual effort, and too much dependence on individual heroics.

Legacy tooling was slowing delivery
Engineering workflows varied across the team
AI usage was ad hoc instead of embedded in execution

The challenge was not just modernization. It was turning the existing SDLC into an AI-native, agentic delivery model without disrupting governance or ownership.

Our Solution

Solution

Eliza helped the company transform its existing SDLC into an Agentic SDLC: an operating model where AI acts as an execution layer across the software lifecycle, not just a side assistant.

First, the team standardized the developer environment, aligned on modern source control, and established shared workflow conventions. In parallel, each engineer was assessed so coaching could be tailored to real workflow gaps and adoption patterns.

Key changes included:

Standardized developer environments
Migration to GitHub Enterprise
Shared branching, review, and CI/CD conventions
AI embedded into coding, debugging, testing, documentation, and review

Eliza stayed advisory and coaching-led throughout, while the client retained ownership of infrastructure and code. By the final phase, the Agentic SDLC had become the team’s default operating model.

Our Results

Results

The engagement materially changed how the company builds software. A 10-week development cycle for a new feature was reduced to 1 week for comparable scoped work.

A 30-person engineering organization now operates with an estimated 5x productivity gain, driven by faster first drafts, shorter debugging cycles, quicker test generation, and less workflow inconsistency. Engineers can move from feature request to an 80–90% complete solution within a couple of prompts.

Highlights:

10 weeks to 1 week for new feature delivery
5x engineering throughput across a 30-person team
80–90% first-pass completion in a couple of prompts

The result was a faster, more consistent, AI-native software delivery model with stronger internal capability and less dependence on a few power users.