Typical use cases
Enterprise knowledge retrieval; document-grounded answers; model access inside applications.
Mercan solution
We prepare AI use on enterprise data for production through capacity, access and model-serving architecture.
Customer problem
What needs to change?
A working prototype cannot safely join enterprise applications without data boundaries and capacity planning.
Who it is for
CTOs and data teams moving AI from pilot to controlled production.
Mercan delivery
From assessment to implementation
What we assess
We review data sources, use case, model requirements and concurrent demand.
What we design
We connect GPU, AI API, RAG, vector data and identity boundaries in the architecture.
What we implement
We build the selected model-serving path and measure response, access and performance on a representative use case.
What we operate
We plan ownership for capacity, versions, usage and incident visibility.
Scope and architecture
What the work includes
Capabilities
GPU/inference; private AI; AI APIs; RAG; vector data; integration.
Technology
GPU, model serving, identity, vector database, APIs and observability.
Delivery model
Feasibility and project work; product and capacity availability verified separately.
Related areas
Continue with relevant detail
Discuss enterprise ai and data platform with Mercan.
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