Enterprise AI Case Study
Enterprise AI Operations Hub
Client: Logix Global Logistics · Segment: Neural Query Pipelines · Published 2026-06-01

Inference Delay
42ms
Time Saved / Week
20+ Hrs
Dispatch Error Rate
0.0%
The Challenge
Logix Global operated multiple disconnected data sheets, transport manifests, and SQL servers containing dispatch logs. Fleet dispatchers spent hours cross-referencing warehouses manually, leading to shipment delays and routing conflicts.
The Solution
We built a unified AI Operations Hub combining manifests, vector embeddings, and RAG execution paths into a natural language search system. The platform runs a lightweight React frontend communicating with containerized FastAPI microservices; natural language queries are validated by parameterized checking loops before SQL execution to prevent command-injection.
The Outcomes
- Instant Dispatch: Routing calculations dropped from hours to sub-minute loops
- Natural Queries: Dispatchers fetch inventory status with plain-language phrases
- Zero Injection: Secure routing loops isolate SQL queries from direct user input
Used Technologies
Next.jsFastAPILangChainOpenAI APIPinecone DBPostgreSQLDockerAWS
