Lighthouse AI
Architecting agentic workflows, embeddings, and predictive sales forecasting pipelines to drive autonomous operations for high-volume enterprise environments.
Role
Principal AI Architect
Domain
Enterprise Data & AI
Tech Stack
Python, NestJS, LLMs, RAG
Core Impact
Autonomous Operations
The Challenge
Enterprise operations were entirely reactive, relying on manual data pulls and static dashboards to forecast sales and identify operational bottlenecks. This lack of real-time, predictive insight led to massive inefficiencies in supply chain management, resource allocation, and workforce scheduling across high-volume environments.
The Architectural Solution
I architected Lighthouse AI to serve as a real-time, autonomous forecasting engine. Moving beyond simple analytics, we built highly scalable RAG (Retrieval-Augmented Generation) pipelines that ingested millions of rows of historical and real-time enterprise data into vector databases.
On top of this data layer, I orchestrated agentic workflows powered by modern LLMs. These autonomous agents continuously analyzed data streams, identifying hidden correlations and generating actionable operational directives without human intervention.
The Result
Lighthouse AI successfully shifted the organization from reactive reporting to autonomous operations. The predictive models drastically improved sales forecasting accuracy, allowing executive teams and ground-level managers to execute data-driven decisions weeks ahead of market shifts, optimizing resource allocation and significantly reducing operational waste.
Ready to deploy agentic workflows?
Let's discuss how Natera Labs can architect custom LLM pipelines to automate your most complex operational bottlenecks.