Model selection
It chooses by task type: short classification goes to the local model, complex reasoning to the larger model.
It picks the right model for each request.
Intelligent Multi-Model AI Infrastructure
A routing layer that chooses between Claude, GPT, Qwen, Nemotron, Gemma and models on your own server for each request. It measures quality, calculates cost, manages quota and switches provider if one goes down.
The problem
Relying on one model is costly and risky.
Tie a product to one provider’s model and you accept three things: its pricing, its quota and its outages. When the price rises, your margin goes. When the quota runs out, users see errors. When the service goes down, your product goes down with it.
And not every job deserves the same model. Running the priciest model to summarise an email is like hiring a digger to hammer in a nail.
What it does
It reviews each request and chooses the right model for that job. Quality holds. The bill drops.
Cost estimate
Track the token cost of every request. No surprises on your monthly bill.
Quota management
It monitors provider limits and reroutes traffic before you reach them.
Automatic handover
If one service slows down or crashes, the request moves to an alternative within seconds. The user never notices.
Measurement
Latency, accuracy and cost are measured continuously. Routing decisions update from those measurements.
Data limit
Requests with sensitive data never leave under any circumstances. They route to the model on your own server.
Who is it for
Teams that do not want to depend on one provider.
Product teams tied to one provider
Systems whose product stops when prices change, quotas fill, or service goes down.
Companies with unpredictable model costs
Teams facing surprise bills at month-end, with no idea how much each feature uses.
Data that partly needs to stay inside
Organizations that must keep some requests inside but cannot run everything on their own servers.
Live demo
Sample run · no real data
One task, six models
- Nemotron·
- Claude·
- GPT·
- Qwen·
- Gemma · local·
Sample run · real measurements are taken at request time
How it works
Four steps.
01
Define
You choose which models can be used and which data cannot leave.
02
Direct
Every incoming request is scored on quality, latency and cost. The winning model is selected.
03
Monitor
Results are measured continuously. If a model declines, its score and traffic drop.
04
Hand over
If there is an outage, routing changes automatically. Not one line of app code changes.
Technology
What it runs on.
- NVIDIA NIM
- TensorRT-LLM
- Nemotron
- Go
- Redis
- OpenAI-compatible API
Local models are packaged as NVIDIA NIM microservices and accelerated with TensorRT-LLM. The same model runs in the cloud or your own data centre through the same interface. The router is written in Go, with metrics and quota status kept in Redis.
Gallery
FAQ
What you may want to know before we talk.
Do we need to change our code?
No. It provides an OpenAI-compatible interface. You only need to change the endpoint for your existing calls.
How do we know which model it goes to?
Every request is logged: which model, how long it took, what it cost, and why it was chosen. The panel shows each one on a separate row.
What happens if a provider goes down?
The request moves to the next suitable model. We log the handoff instead of hiding it, so you can measure the quality difference later.
Can we keep specific data from leaving?
Yes. You set a rule: send this content type only to the model on our server. The rule cannot be bypassed. The router never sends that request outside.
Can I buy it now?
No, still in development. The early access list is open.
Contact
Something like this?
do you want to build?
Tell us in one sentence. We will explain what you need and how long it will take.