Client Success Stories

Challenge:

Alpha School aimed to design a "school of the future", leveraging cutting-edge learning methodologies. To make informed technology choices, we needed a deep understanding of modern learning platforms, their pedagogical foundations, and how they structure knowledge representation.

Solution:

We conducted teardowns of multiple learning management systems (LMS) and adaptive learning platforms, focusing on:

• Knowledge graphs – How concepts are structured, linked, and reinforced over time.
• Adaptive learning – Personalization models for student progression.
• Cognitive load theory – Ensuring engagement without overburdening learners.
• Mastery-based progression – How systems enable competency-based learning.

Outcome:

• Provided actionable insights on best practices in digital learning.
• Helped Alpha School design a next-generation education model informed by leading research.
• Ensured technology choices aligned with modern pedagogy while avoiding the pitfalls of rigid, one-size-fits-all platforms.

This teardown-driven strategy ensures educational innovation is rooted in deep, technical, and pedagogical understanding—not just tech hype.

Challenge:

In 2023, we built an agentic AI workflow to assist in resolving software bugs—an approach that was considered cutting-edge at the time. The system integrated multiple AI agents, including quality control (QC) agents, to analyze, categorize, and suggest fixes for software defects. While it successfully resolved simple issues, it struggled with more complex cases requiring logical reasoning and deep system understanding.

Solution:

The project revealed a fundamental limitation: current AI models excel at pattern recognition but lack true reasoning capabilities. Despite advancements in agentic workflows, the system hit a hard wall in AI's ability to handle nuanced debugging. Our insights aligned with emerging research on the limitations of AI reasoning:

• Challenges in Automated Reasoning with LLMs
• Limits of AI in Software Debugging
• Evaluating Multi-Agent AI Collaboration

Outcome:

• Automated resolution of simple issues, reducing engineering effort on low-value debugging.
• Identified the AI reasoning gap, shaping future AI development strategies.
• Refined AI's role in engineering, focusing on assisting rather than autonomously solving complex bugs.

This experience underscored a critical insight: while AI can augment software engineering, true reasoning and problem-solving remain human-dominated domains—at least for now.

Challenge:

An enterprise SaaS platform was running analytics on a legacy "big data" architecture designed in the early 2000s. Over time, the volume of stored data ballooned, making traditional querying expensive and inefficient. The system relied on an outdated big data stack, leading to skyrocketing AWS costs and slow query performance.

Solution:

Re-architected the analytics pipeline by replacing the legacy big data processing stack with Amazon S3 and Athena. Migrated historical data to S3 with optimized partitioning and leveraged Athena for serverless SQL querying. This eliminated the need for costly, always-on infrastructure while maintaining performance.

Outcome:

• 96% cost reduction in analytics operations.
• Improved query performance, with some queries running 10x faster.
• Eliminated infrastructure maintenance overhead, enabling a fully serverless and scalable solution.

This transformation proved that what was considered "big data" in 2000 is not big in 2020—modernizing with cloud-native solutions can drastically cut costs and improve efficiency.