Legal Tech Company Builds Governed AI-SDLC

Client:

Legal tech software company

Time to First Value:

1 month

Users Enabled:

Our Problem

Problem

The company had already tried to bring AI into engineering. Developers had access to AI coding tools, additional tools had been layered into the environment, and external support had been brought in. But the impact was uneven because the underlying delivery process had not changed.

The business was operating with a legacy monolith, tightly coupled components, slow compile times, regression risk, and handoffs between product, engineering, and QA that created friction. QA was still operating downstream instead of in parallel with development. Requirements were not consistently structured for AI-assisted execution, and teams did not have a shared model for how AI-generated plans, code, tests, and pull requests should be governed.

The core problem was not tool access. It was the absence of an operating model that made AI usage structured, testable, and accountable across the software delivery lifecycle.

Our Solution

Solution

Eliza helped the company design and implement a governed AI-enabled software delivery lifecycle, or AI-SDLC, that fit into its existing product and engineering workflows.

The work focused on the operating model around AI-assisted delivery:

End-to-end AI-SDLC workflow documentation across planning, implementation, pull requests, review, remediation, and validation
Prompt and workflow patterns for product, engineering, and QA teams
Governance standards for AI-generated plans, code, tests, and pull requests
Architecture and integration guidance for the company's existing environment

The model used the company's current delivery systems rather than creating a separate AI process. Jira and plan files became the source of truth, while AI-assisted workflows supported ticket generation, branch creation, implementation, PR review, remediation, and validation.

Eliza also helped the team shift quality earlier in the process. Human approval points, audit expectations, logging, and QA validation were built into the workflow so AI could increase delivery speed without removing accountability.

Our Results

Results

4 days of AI Foundry work delivered what leadership described as roughly 1 month of sprint output
600 Playwright tests generated through AI-assisted QA workflows
6 of 6 SOW deliverables completed
8 repositories running the new AI-SDLC process
Proposed delivery model reduced staffing from 30 engineers to 9 people across pods
Target set for 50% story point velocity improvement over the next 2 sprints

The AI Foundry exercise became the clearest proof point. A small team applied the new workflows to real delivery work and moved an intake form builder through QA and UAT readiness for customer launch. The end-to-end PRD-to-ticket-to-agent execution pipeline was confirmed as adopted, with agent runs stored in repos and development work connected back to requirements.

The cultural shift was as important as the technical one. Product, design, engineering, and QA began working around a shared delivery model. Stakeholders were willing to let AI accelerate decisions and implementation where the governance model made the tradeoffs clear. Leadership summarized the shift simply: the team had momentum, the experiment had worked, and the next step was to scale it.