Typical use cases
Enterprise knowledge retrieval; model calls within applications; inference on private data.
Mercan service
Mercan designs enterprise infrastructure for GPU workloads, model inference, private AI and governed data access.
Customer problem
What needs to change?
Capacity, data boundaries and operating cost can remain unclear when a model experiment moves toward production.
Who it is for
CTOs, data and application teams moving enterprise AI toward production.
Mercan delivery
From assessment to implementation
What we assess
We evaluate model size, concurrency, data access, security and compute demand.
What we design
We plan GPU capacity, inference, AI APIs, RAG and vector data layers for the use case.
What we implement
We build the selected infrastructure and model-serving path, then measure integration and performance.
What we operate
We define coverage for capacity, access, usage and model-service health.
Scope and architecture
What the work includes
Capabilities
GPU infrastructure; inference; private AI; LLM infrastructure; AI APIs; RAG architecture.
Technology
GPU, containers, model serving, identity, vector databases and observability are considered together.
Delivery model
Technical feasibility and project delivery; service availability is verified separately.
Service capabilities
Specific work in this area
GPU Infrastructure
We plan GPU infrastructure around the AI workload's memory, compute, network and storage needs.
Private AI Infrastructure
We design model access, data boundaries and compute capacity for an enterprise environment.
LLM & Inference Infrastructure
We plan resources, scaling, access and observability for model serving.
RAG & Vector Data Architecture
We design ingestion, vector indexing, permissions and model connections for enterprise content retrieval.
Related areas
Continue with relevant detail
Discuss ai infrastructure and platforms with Mercan.
Tell us about your current environment and the result you need.