Nexus-7: The Corporate Memory Core

Inspired by the implanted memories of 'Blade Runner's' Replicants and the vast AI-driven data networks of 'Hyperion', Nexus-7 is a cloud-native solution designed to combat 'corporate amnesia'—the critical loss of knowledge when employees leave or projects evolve over time. It functions as an AI-powered corporate archaeologist, building a living, intelligent history of a company's digital life.

The Story & Concept:
Every company has a soul, a history written in the digital exhaust of Slack channels, Jira tickets, code commits, and design documents. Like the Tyrell Corporation implanting a past into a Replicant to create stability, Nexus-7 gives a company a coherent, accessible past. It prevents teams from repeating old mistakes and enables new hires—or even new AI agents—to be 'implanted' with years of context in minutes. It transforms a chaotic mess of data into a queryable oracle, a private Technocore that understands not just -what- was done, but -why-.

How It Works (The Cloud Architecture):

1. The Ingestion Weave: A suite of secure, serverless cloud functions (e.g., AWS Lambda, Google Cloud Functions) act as connectors. They are granted read-only access to a company's tools. On a schedule or triggered by events, they ingest data: conversations, documents, tickets, code, and their metadata. This is the 'AI Workflow' scraper, but turned inward, focusing on internal, unstructured data.

2. The Mnemonic Core: All ingested data is funneled into a low-cost object storage service (like Amazon S3). Another set of serverless functions process this raw data. Using Natural Language Processing and LLMs, they extract key decisions, technical arguments, project timelines, and the people involved. This processed information, along with the original text, is converted into vector embeddings and stored in a managed vector database. This forms the 'memory'—a high-dimensional map of the company's entire history.

3. The Oracle Interface: Users interact with Nexus-7 through a simple chat interface or an API. They can ask natural language questions like, 'Why did we choose Kafka over RabbitMQ for the payments service in Q3 2022? Summarize the main arguments and link to the Slack discussion.' Using a Retrieval-Augmented Generation (RAG) model, the system queries the vector database to find the most relevant 'memories' and then uses an LLM to synthesize a concise, accurate answer, complete with citations and links to the original source material.

This architecture is inherently low-cost (pay-per-use), scalable, and easy for a solo developer to build and maintain by leveraging managed cloud services. The business model is a simple SaaS subscription, tiered by data volume or number of users, offering immense value by preserving a company's most valuable and elusive asset: its collective memory.