Inspiration
Organizations struggle with fragmented performance data scattered across multiple platforms—Slack conversations, Jira tickets, email threads, and meeting notes. The inspiration came from recognizing that valuable insights about employee collaboration and performance are hidden in everyday workplace interactions, but remain inaccessible due to data silos. SuperMemory's ability to create a unified knowledge graph from diverse sources presented an opportunity to automatically track, analyze, and visualize employee performance in real-time without manual effort.
What it does
The system automatically collects data from workplace platforms (Slack, Jira, email, and meeting transcripts) and sends it to SuperMemory to create intelligent connections between employees and tasks. It uses large language models enriched through OpenRouter to analyze these connections and memories, generating comprehensive summaries of each employee's collaboration patterns and performance metrics. The results are displayed on an interactive dashboard where managers can extract meaningful insights about team dynamics, individual contributions, and performance trends—all without manual data entry.
How we built it
The project implements a five-stage pipeline: First, we ingest JSON exports from Slack, email, meeting notes, and Jira into the system. Second, we send this data to SuperMemory, which breaks it into semantic chunks and creates a dynamic knowledge graph with intelligent connections between employees and tasks. Third, we retrieve these memories and relationships from SuperMemory's API. Fourth, we enrich the data using OpenRouter's LLM capabilities to summarize collaboration patterns and performance indicators. Finally, we visualize the AI-generated insights on a dashboard interface that makes the information actionable for decision-makers. The architecture leverages SuperMemory's sub-300ms recall speed and intelligent relationship inference capabilities to process large volumes of workplace data efficiently.
Challenges we ran into
One major challenge was working with synthetic data fabricated using PerplexityAI rather than real company data, which limited the depth and authenticity of our results. Integrating multiple data sources with different formats and structures required careful handling to ensure SuperMemory could create meaningful connections across platforms. We also faced complexity in designing prompts that would generate comprehensive performance summaries from the raw connections and memories without losing important contextual nuances. Additionally, building a production-ready pipeline while managing hosting and deployment constraints required strategic architectural decisions.
Accomplishments that we're proud of
We successfully created an end-to-end automated performance analysis pipeline that transforms scattered workplace data into actionable insights. The system leverages SuperMemory's sophisticated features—including smart forgetting algorithms, temporal understanding, and implicit connection inference—to perform deep contextual analysis beyond simple keyword matching. We achieved a production-ready codebase that demonstrates the practical application of AI-powered knowledge graphs in workplace analytics. The project showcases how modern memory APIs can break down data silos and enable complex analysis at enterprise scale with minimal technical setup.
What we learned
We discovered that SuperMemory's brain-inspired architecture—with features like intelligent decay, recency bias, and context rewriting—is far more powerful than traditional vector databases for workplace analytics. The ability to infer implicit connections between data points that were never explicitly documented proved crucial for understanding true collaboration patterns. We learned that automatic, real-time memory updates integrated seamlessly into existing workflows are essential for adoption; manual data entry systems fail in practice. Additionally, we gained insight into how hierarchical memory layers and smart forgetting algorithms keep AI responses sharp and relevant by preventing information overload.
What's next for Untitled
The immediate next step is implementing the system with real company data rather than synthetic data to validate and enhance the quality of insights. We plan to host the entire pipeline in a production environment with proper infrastructure and scaling capabilities. Automating the data ingestion process through proper API integrations with Slack, Jira, email systems, and meeting platforms will eliminate manual file uploads. Long-term, we envision expanding SuperMemory to become the "mind of the company"—continuously tracking all interactions and providing real-time performance analytics, predictive insights about team dynamics, and proactive recommendations for improving collaboration and productivity.