Fetcherr builds a next-generation AI decision engine on Google Cloud for real-time market optimization
Legacy industries relied on human intuition, static rules, and fragmented workflows to make business decisions, which could not account for the constantly shifting landscape of competitive pressure, consumer behavior, operational constraints, and external events.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Market data ingestion
Fetcherr processes massive volumes of internal and external data, including historical sales, real-time demand, competitive prices, macroeconomic indicators, and operational capacity.
Tools used
Large Market Model (LMM)Vertex AIBigQueryGoogle CloudCloud Identity-Aware Proxy (IAP)Cloud StorageDataflowGoogle Kubernetes Engine
Outcome
Fetcherr's LMM running on Google Cloud automates complex business decisions, reducing manual workloads by 60–80%, achieving revenue uplift of more than 10% in optimized revenue streams, and delivering 95%+ pricing accuracy with continuous AI adjustments. Client onboarding time was reduced from years to weeks.
What failed first
Traditional tools analyzed data only retrospectively and could not forecast, optimize, or act in real time, leaving pricing, inventory, and operational decisions siloed and unable to adapt to market volatility.