Success Story

Vector Solutions provides enterprise SaaS software for training management, compliance, and operational readiness, spanning more than 7,000 safety and compliance courses. The courses are built by 490+ subject matter experts and used by over 24,000 customers across the public and commercial sectors.

Vector Solutions needed to scale course production and engineering delivery without adding headcount, while keeping subject matter experts and engineers in control of every output.

• Reduce reliance on manual, SME-only course drafting that capped how fast the 7,000+ course library could grow.
• Guarantee data privacy and domain accuracy for safety and compliance critical course content, ruling out generic AI tools.
• Modernize a 200+ person engineering engagement running largely manual SDLC processes across product engineering, UI, and platform modernization work.
• Prove AI adoption was producing real time savings and quality gains, not just licenses and anecdotes.
• Track engineering AI maturity and spend at the individual, team, and model level rather than as a blanket rollout.
• Maintain human review and sign-off at every stage, from course publication to code merges.

We implemented two connected Claude-based solutions, one for content generation and one for the SDLC, both grounded in Vector's own data and governed by human review.

• Indexed Vector's proprietary course material using Amazon Titan Text Embeddings and a FAISS vector database for retrieval-augmented generation.
• Deployed Claude Sonnet 4 on Amazon Bedrock in a private sandbox to generate each course component as an independent, modular call.
• Delivered content through FastAPI middleware into Vector's CMS, with every script SME-reviewed before publication.
• Introduced Claude Code after an initial GitHub Copilot pilot, reaching roughly 200 engineering users.
• Built an engineering maturity model (L1 to L5) around Claude Code capabilities, from CLAUDE.md and Git workflows to multi-agent production use.
• Deployed an agent library across the SDLC (PRD, design-to-code, code review, CI/CD, defect agents), each with a named human checkpoint.
• Built Engineering Insight Sphere to measure adoption, acceleration, cost, and delivery flow, with monthly per-team token limits.

• Faster course production cut cycle time 35 to 40% and SME manual effort 60%
• Higher throughput increased course output 3x, holding accuracy above 95%
• Engineering productivity gain of roughly 25%, tied directly to Claude's introduction into the SDLC
• Deeper Claude Code adoption to 70.8% of 200+ engineers reaching L3 or above, up from 24.7% at baseline
• Time savings of 2,200 hours across 300+ tickets, a 37.1% average saving per ticket
• Faster delivery flow, with median epic cycle time down 58% and lead time down 41%