SAM Assist

The Secure Access Management (SAM) platform is the identity backbone for 85+ internal products, serving 1.8 million users and supporting a portfolio with a combined annual revenue of $2 Billion+.

However, as we scaled, our engineering team became trapped in “Shadow Support.”

• The Cost of Complexity: New product teams integrating with SAM found our documentation dense and complex.
• The Drain: The team was fielding 5-8 unsolicited Teams pings daily and losing 6-8 hours per week on “Consultation Calls.” This was untracked work that distracted senior engineers and support from shipping core features.
• The Realization: We could not scale the platform to the next 50 products if we had to hand-hold every integration. We needed a way to free up our best engineers and support team members' time by automating flows wherever possible.

I defined a vision to shift integration support from Manual Consultation to AI-Driven Self-Service. We built SAM Assist, a Generative AI chatbot embedded directly into our developer portal. It allows product teams to ask complex integration questions (e.g., “How do I implement OIDC for my intelligence reports product?”) and get instant, code-ready answers based on our documentation.

This wasn’t just a wrapper around ChatGPT. We built a robust Retrieval-Augmented Generation (RAG) system to ensure accuracy and data security. This was built on top of Spark Assist platform.

• Knowledge Base: We initially fed the model our technical PDFs.
• Automation: We later evolved the pipeline to integrate directly with Confluence. Now, whenever we update a documentation page, the system automatically re-indexes and updates the Vector Embeddings.
• The Result: The AI is always in sync with the latest API changes, requiring zero manual retraining.

I partnered with the internal Spark Assist team to leverage their foundational AI models while my team provided the domain-specific data structure.

• The Pitch: I secured leadership buy-in by framing this not as a “cool AI project,” but as an Efficiency Lever. I demonstrated that “Shadow Support” was effectively costing us one Full-Time Employee (FTE) in lost productivity.
• The “Docs” Win: This also solved a user experience problem. New product teams no longer had to read 50-page wikis; they could simply query the bot to unblock themselves, significantly reducing their Time-to-Hello-World.

• Zero-Headcount Scaling: We successfully onboarded new products to the platform without increasing our team size. The AI absorbed the complexity of the added volume.
• 40% Support Reduction: We saw a drastic drop in repetitive “How-to” tickets and DMs.
• Strategic Focus: By reclaiming the 6-8 hours/week previously lost to consultations, my engineering team shifted focus to high-value tasks, reducing turnaround time on critical platform upgrades.