Case Study: How SEP Uses AI Across the Software Delivery Process

Founded in 1988, SEP is a software product design and development firm based in Westfield, Indiana. SEP builds custom software across a wide range of platforms including cloud, AI, Data, embedded systems, mobile, and web; all tailored to their clients’ needs. Their clients come from a variety of industries including aerospace and defense, automotive, heavy machinery, agriculture, medical and life sciences, IoT, fintech, and more.

When most people think about AI in software development, they imagine tools that help engineers write code faster. Yet code-writing accounts for only about 8% of the total software delivery lifecycle. The remaining 92%; planning, coordination, testing, reviews, and release processes dominates time and cost. SEP discovered that the real opportunity for AI wasn’t just speeding up, but in transforming how work happens across the entire delivery process.

Industry Context

Large-scale engineering projects often face delays and unexpected issues because critical problems are uncovered late in development. Traditional Agile methods expect this, relying on iterative discovery. Meanwhile, tooling gaps, manual analysis, and costly handoffs slow progress. In a $20+ million client project spanning six teams and responsible for designing structural components with real-world safety implications, these inefficiencies become both expensive and risky.

AI Strategy

SEP explored AI not just as a code-generation tool, but to rethink the economics of software delivery. Their strategy involves using AI to surface unknowns early, automate specialized analysis, empower engineers to generate custom tools on demand, and shift discovery work from mid-project crisis mode to early-stage planning. This reframed experimentation as ‘practically free,’ enabling teams to challenge long‑held assumptions about what work is required and when.

Solution

Across discovery, development, and release orchestration, SEP built dozens of AI‑powered tools that fundamentally changed team workflows.

Discovery & Planning

AI-assisted planning helped teams identify missing requirements, ambiguous integrations, and knowledge gaps before development began. AI also analyzed project management data to visualize FlowTime and categorize features that previously would have required many hours/days of work but was completed by a software engineer in one day.

Development & Execution

Engineers used AI to generate custom tools on demand, such as a structural file visualization tool created in under five minutes work that would have required weeks of engineering effort. AI-guided clarifying questions ensured accuracy while enabling self‑service problem‑solving.

Release & Orchestration

AI automated the creation of release comparison reports, identifying every feature toggle change across versions. This shifted release work from a time-consuming discovery exercise to a streamlined review process, reducing hours of coordination to minutes.

Outcomes

The overall impact was a fundamental economic shift: work that once took months now takes hours; expert-only analysis can be done by less time-constrained people; and teams moved from “Can we afford to explore this?” to “What problem should we solve next?” Previously infeasible problems became trivial and worth solving.

AI in SEP has transformed coding productivity, and importantly,  the entire software delivery value stream. It changed how teams plan, analyze, build, and release software, turning once‑accepted constraints into solvable problems. By targeting the overlooked 92% of software delivery, SEP is only beginning to uncover what’s truly possible.

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