Your workload. Your hardware.
A continual search for better performance,
within the limits that matter to you.
In development For enterprise infrastructure & local AI.
A configuration is a decision made for a moment in time. Models change. Demand shifts. A new device opens up different possibilities.
We’re building an agent that runs in your environment, learns from real usage, and searches for improvements across the inference stack. Every proposed change comes back to one question: does it work better for you?
Recursive self-improvement, grounded in
measurements you can inspect.
Understand arrival patterns, input and output lengths, cache reuse, and latency. Find the limiting stage before choosing what to change.
Request counts tell only part of the story. Token shape and arrival timing matter.
Explore compatible model implementations, hardware, kernels, parallelism, and serving settings. Spend the experiment budget on promising candidates.
The best candidate depends on your model, device, traffic, and service targets.
Compare candidates with the baseline. Check correctness, task quality, latency, memory, and total cost on representative and holdout workloads.
A faster kernel is only a win when the full service meets your requirements.
Review a tested recommendation, apply it through your change process, and check the outcome. Retain what worked and re-evaluate when conditions change.
Controlled automatic rollout is planned as a later, opt-in capability.
Connected decisions across the stack.
Explored together, measured end to end.
Understand what makes your model different.
Architecture-aware attention and operator paths, expert routing, state layouts, and approved model transformations. Changes to model behaviour need explicit quality checks.
Put the right work on the right resources.
Accelerator choice, GPU and node placement, tensor and expert parallelism, replicas, and prefill–decode separation. Each hardware and software combination needs its own validation.
Improve the code doing the work.
Select, generate, compile, and measure hardware-specific kernels. Explore layouts, fusion, quantisation, graph execution, and overlap, with independent correctness checks before full-engine evaluation.
Adapt execution to your changing traffic.
Continuous batching, chunked prefill, admission, cache-aware routing, speculative decoding, offloading, and capacity planning. Measure goodput and tail latency together.
Planned scope. Each model, engine, and hardware target needs separate validation.
Get more from the AI you serve,
at the scale you operate.
Continual optimisation for private AI infrastructure, with service targets, reviewable evidence, and changes that follow your operating rules.
A lightweight optimisation loop for your own machine. Find a better balance of response speed, context, quality, and available memory.
Both offerings are in development. Runtime support and rollout plans are being defined.
The goal is a better measured decision, with the context to understand it and the control to decide what happens next.
Read the thinking behind the searchRepresentative traffic, correctness checks, and service targets. Uncertainty belongs in the report.
Account for serving resources, experiments, compilation, and the optimisation agent itself.
Start with recommendations. Review the evidence and apply changes through your own process.
A bounded experiment loop: observe, propose, test, and learn. Results from each iteration inform the next search. The agent works within your targets, permissions, and budget; it cannot rewrite its own evaluator or operating controls.
The service is in development. We are defining the first supported runtimes and hardware targets. Contact us about an enterprise design partnership or individual local-AI interest. There is no public installer or committed launch date yet.
The intended design keeps observation and experiment data in your environment, with raw prompts and responses excluded from collection by default. Any export, replay capture, portal sync, or external agent-model call must be explicitly configured. Fully local operation remains a capability to validate.
The initial workflow produces tested recommendations for your review. Controlled automatic application is planned as a later opt-in mode, with approval rules, health checks, and rollback. On shared local machines, experiments need resource limits and suitable execution windows.
The search is designed to span model architectures, NVIDIA B200/B300, AMD accelerators, custom inference chips, and local hardware. These are target platforms, not a current support list. Each model, engine, device, and toolchain combination must pass compatibility and correctness checks.
There is no universal configuration or guaranteed saving. We aim to find the best measured feasible candidate within an agreed search budget. Reports should include experiments and agent costs, uncertainty, and cases where no improvement is found.
Tell us what you serve, where you run it,
and what better would look like.
navid