Data cannot leave
Run sensitive inference inside your VPC, on your hardware, or directly on the device.
A model that fits the job.
We help teams move bounded AI workloads off oversized frontier models and into custom systems they can own, measure, and run where the product needs them.
Privatekeep sensitive inputs inside your environment
Responsiveremove the network round trip where latency matters
Ownedcontrol the weights, runtime, release path, and economics
Start with the constraint
Nobody needs “distillation.” They need the problem it solves.
The best model project begins with a measurable task and a real constraint. It does not begin with a preference for a particular architecture.
The network is too slow
Move time-critical decisions closer to the user, machine, or simulation that needs them.
Volume broke the economics
Stop paying frontier-model prices for a narrow, repeatable workload with a bounded output space.
The cloud is unavailable
Support products in intermittent, remote, vehicle, field, or on-device environments.
The provider is the risk
Reduce exposure to changing pricing, capacity, deprecations, rate limits, and external policy.
The task is narrower
Build for the workflow you actually have instead of the open-ended universe a frontier model is designed to cover.
Our delivery model
Measurement is the first product.
A smaller model is only better if it clears the quality bar on your task, on your hardware, under your operating conditions. We define that bar before training begins.
Choose the task
Screen the workload for bounded outputs, sufficient volume, usable data, and a success criterion that can be locked.
Build the evaluation set
Create a human-verified, versioned benchmark that never overlaps with training and establishes the teacher baseline.
Train + compress
Select the student, assemble clean teacher data, distill the target behavior, quantize, and convert to the target runtime.
Benchmark the real target
Measure quality, memory, latency, power, throughput, and thermals on the hardware where the model must live.
Deploy + monitor
Integrate the model, route long-tail work safely, stage the rollout, and watch for drift against the locked evaluation set.
Use a frontier model to discover the product. Use the right-sized model to ship it.ReBlink model strategy
Beyond games
Built for workloads with a scoreboard.
Our game-agent work taught us to evaluate AI against outcomes. The same discipline travels well.
01
Structured extraction
Turn high-volume documents into schema-valid outputs with a task-specific quality measure.
02
Classification + routing
Move repetitive triage and intent decisions to a small model with a clear answer space.
03
Real-time scoring
Put anomaly, safety, or control decisions on the device when remote inference cannot meet the latency budget.
04
On-device assistants
Deliver focused help where privacy, offline operation, and product responsiveness are requirements.
One workload. One locked benchmark.
Find out if a smaller model can win.
Start with a focused fit check. We will tell you whether the task is a credible candidate and where the project should stop if it is not.
Request a fit check