For platform teams · the inference layer

Same hardware.
Twelve times
the reach.

ThinkCore runs your own models inside your own walls, with inference tuned for the way Idrak reasons over enterprise data. On-premise AI stops being a cost problem and starts being a platform.

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The economics

On-premise AI rarely fails on capability. It fails on the bill.

0x
More users, same GPUs
Concurrency is a scheduling problem before it is a hardware problem. ThinkCore serves twelve times the users on the cluster you already own.
0%
More efficient token usage
For YFlow's understanding operations, ThinkCore cuts the tokens needed to reach the same answer by 65%, because it knows the shape of the work.
How it gets there

Four levers, pulled together.

Generic serving stacks treat every request as a stranger. ThinkCore assumes the opposite: enterprise questions repeat, share context, and mostly do not need your largest model.

01 · Scheduling
Continuous batching
Requests join and leave a running batch instead of waiting for one. The GPU stops idling between turns, which is where most on-prem capacity quietly goes.
02 · Memory
Shared context cache
Your semantic layer and schema context are identical across thousands of questions. ThinkCore keeps that prefix resident instead of re-reading it every call.
03 · Routing
Right model per step
Resolving an entity name is not the same job as writing a recommendation. Cheap steps go to small models, and only genuine reasoning reaches the large one.
04 · Compilation
Quantized, fused kernels
Weights are quantized and kernels fused for your specific accelerators, so throughput comes from the silicon you have rather than the silicon you would need to buy.
Built for the workload

It knows what YFlow is about to ask.

Understanding a business is a repetitive pattern: resolve entities, check definitions, traverse the graph, then reason once at the end. ThinkCore is tuned around that shape, which is where the 65% comes from. It serves any model you bring, and it is compatible with every Idrak product.

See how YFlow reasons →
One question, routed
Resolve entity names small
Check metric definition cached
Traverse the graph no model
Reason and recommend large
One large-model call instead of four
Where it runs

Nothing leaves
your walls.

Your infrastructure
On-premise or your private cloud. Data, prompts and model weights stay inside the perimeter you already control.
Your models
Bring open weights or your own fine-tunes. ThinkCore optimizes serving without locking you to a vendor's model.
Your audit trail
Every call is logged with the model, the route it took and the tokens it cost, so finance and security read the same record.

Run the numbers
on your own cluster.

Tell us your hardware and your concurrency target. We will show you what ThinkCore changes before you commit to anything.

Book a demo
Idrak YFlow Insights
Idrak · idraklab.com