Neogov is a US-based provider of cloud workforce management software for more than 13,000 government agencies, educational institutions, and public safety departments. Its platform supports the full employee lifecycle, including recruiting, onboarding, learning, performance, scheduling, and HR operations.
Location:
USA
Industry:
Software Development
Partnership period:
February 2025 - present
About the customer About the customer
Business challenge Business challenge
Neogov aimed to expand its analytics, data science, and AI capabilities while meeting public sector organizations' security and compliance expectations. Its teams were already using AI tools, including Claude Code and GitHub Copilot, for everyday coding activities.
Before the program, cross-system root-cause analysis typically took two to three days. Stored procedure analysis and blast-radius mapping required four to five days, while certain impact-assessment tasks could not be automated reliably.
Neogov also wanted a measurable and secure approach to scaling AI adoption. As a StateRAMP-authorized organization working toward FedRAMP authorization, it required a rigorous security review for external AI tooling and controlled access to relevant engineering context.
N-iX approach N-iX approach
Because Neogov’s engineers already used Claude Code and GitHub Copilot in their daily work, N-iX focused on additive workflows that could address complex engineering tasks beyond routine code generation. N-iX applied its proprietary APEX framework to introduce structured GenAI enablement across Neogov’s software development lifecycle.
N-iX integrated the Model Context Protocol (MCP) into Neogov’s systems, allowing Claude Code to access approved database context securely. This enabled engineers to investigate root causes using system evidence and aligned the solution with Neogov’s security and compliance requirements. The engagement followed four stages:
- Assess. N-iX reviewed Neogov’s SDLC and existing AI use across four engineering teams, representing approximately 30 engineers. The team identified AI Champions and established baseline metrics for AI adoption, throughput, delivery speed, and quality.
- Pilot. N-iX worked with the AI Champions to design, build, and test Claude Code workflows on real engineering tasks. The work combined high-impact quick wins with custom solutions and introduced secure MCP connectors for database access.
- Expand. After the AI Champions validated the workflows, N-iX made them available to broader delivery teams, including QA. The program expanded adoption of proven workflows and introduced additional use cases. The work included a desktop-to-web modernization platform, a scheduling service, a mobile field-operations assistant, and a shared routing service. N-iX delivered role-specific workshops on Claude Code, GitHub Copilot, and Gemini, then connected the relevant tools to WorkWave’s DX platform for automatic metrics collection.
- eXcel. N-iX established ongoing measurement across AI adoption, throughput, speed, and quality. The team also began implementing more advanced, agentic workflows to support continuous improvement.
The Assess and Pilot stages took approximately 11 weeks, after which the program moved into the Expand phase. In total, N-iX built 16 AI workflows for acceptance-criteria writing, test-case generation, cross-system debugging, stored procedure and blast-radius analysis, and process documentation.
Production & adoption Production & adoption
The program has been live since February 2025 and remains active. Following the 11-week Assess and Pilot phases, validated workflows moved into the eXpand phase and were made available for broader team adoption, including QA.
The initiative supports four engineering teams, or approximately 30 engineers, and is embedded in Neogov’s existing SDLC environment, including Jira, Confluence, and Bamboo. Claude Code workflows support daily work such as production issue investigation, pull-request reviews, process documentation, test design, and dependency analysis.
N-iX continues to monitor AI adoption, throughput, speed, and quality. The ongoing partnership has expanded to include additional N-iX engineers, supporting the continued development and adoption of these capabilities.
Results
Measured across participating teams during the three-month program, the initiative established a repeatable GenAI adoption model and demonstrated improvements in development workflow efficiency.
- Developer time saved: 41 to 57 developer-days per month across 12 measured developers, equivalent to 450 to 630 developer-days annually if the measured monthly rate is sustained (monthly team tracking during the eXpand phase).
- Cross-system root-cause debugging: two to three days → 16 minutes per incident, a reduction of more than 95% (measured across production debugging cases after rollout).
- Stored procedure analysis and blast-radius mapping: four to five days → approximately one day, a 75% to 80% reduction (team time tracking).
- Automated blast-radius mapping: no repeatable automated workflow → two-minute analysis for applicable use cases through an MCP-connected Claude Code workflow (workflow measurement).
- WDMS Policy team: 20 to 25 developer-days per month saved on pull-request reviews (team-level tracking).
- Learn team: 19 to 29 developer-days per month saved on automated documentation and debugging (team-level tracking).
The DMS Policy and Learn teams demonstrate how the program applied AI to distinct development activities. The DMS Policy team saved approximately 20 to 25 developer-days each month in pull-request reviews, while the Learn team saved approximately 19 to 29 developer-days each month across documentation and debugging workflows.
“Watching a debugging task that used to take two or three days drop to 16 minutes changed how the team thought about database access altogether. Engineers stopped guessing at root cause and started proving it, and that shift showed up everywhere else in the program too,” said an N-iX engineer working with Neogov.
Stack
- Claude products: Claude Code, integrated through Model Context Protocol (MCP) connectors for secure, approved database access.
- Also in use: GitHub Copilot.
- Environment: .NET 4 and .NET 8, SQL Server, Bamboo, Jira, and Confluence.
Services:
GenAI implementation, AI consulting, MCP integration, Engineering process optimizatio
Expertise delivered:
GenAI Implementation
Technologies:
Claude Code, Model Context Protocol (MCP), GitHub Copilot, Microsoft SQL Server, .NET 4 and .NET 8
Contact us
Briefly outline your project or challenge, and our team will respond within one business day with relevant experience and initial technical insights.
Trusted by
Our partners
Compliance
