What We Do

Production AI
Systems Engineering.

We don't consult. We build the layer between your AI ambition and your production reality – then operate it.

Services

Four areas.
One coherent
stack.

Each service operates independently or as part of a fully integrated engagement. All of them are rooted in the same conviction: AI only creates value when it runs reliably in production.

01

AI Performance

Service Clarity

End-to-end performance engineering across the inference stack. We target latency, throughput, and cost simultaneously – without compromising model quality.

Inference OptimisationThroughput ScalingCost Reduction
02

LLM Architecture

Define Scope

Model architecture selection and co-design. We evaluate the tradeoffs between foundation models, fine-tuning, and custom training – then build the right system for your constraints.

Model SelectionFine-tuningCustom Training
03

Stack Optimisation

Efficiency

Full-stack observability and systematic optimisation. From GPU memory hierarchy to application-layer caching – we find and remove every unnecessary millisecond and dollar.

CUDA KernelsMemory TuningCost Profiling
04

Knowledge Transfer

Capability Building

We don't create dependency. Every engagement is structured to leave your team more capable than we found them – with documentation, runbooks, and embedded training.

Team UpskillingRunbooksEmbedded Training
How We Work

Diagnostic → Intervention → Redesign.

01

Diagnostic

Model LayerArchitecture & weightsRuntimeServing & orchestrationCUDA KernelsExecution layerMemory / HBMBandwidth bottleneckHardwareGPU siliconBOTTLENECK

A structured technical audit across your full inference stack – from CUDA kernels to serving topology – before touching a single line of code.

02

Intervention

BEFORE → AFTERLatency p99high–61%Throughputlow+2.4×GPU MFUlow+2.4×KERNEL REWRITE APPLIED

Targeted engineering at the highest-leverage layer. We operate where the metric moves fastest – not where it's easiest.

03

Redesign

OLD ARCHITECTUREMonolithSingle GPUBottleneckNEW ARCHITECTURERouterShard AShard BCacheServeClient

Where intervention isn't enough, we rebuild. New architecture, new hardware strategy – fully documented and handed to your team.

Every engagement begins with a Diagnostic
– never a proposal.

We've never seen two production AI systems with the same problem. Generic proposals produce generic solutions. We start with what's actually wrong – then scope from there.

Audience

Infra teams.
ML platform teams.
CTOs.

01

Infra Teams

App LayerRuntimeGPU KernelAI WORKLOAD NOT DESIGNED FOR THIS STACK

Carrying AI workloads on infrastructure that wasn't designed for them. We embed at the hardware and systems layer – not above it.

02

ML Platform Teams

DataTrainEvalServeProductSERVING LAYER BLOCKING DOWNSTREAM TEAMS

Building the internal platform that every AI product team depends on. We accelerate the critical path and remove the performance ceilings you didn't build.

03

CTOs

AmbitionRealityGAP WIDENS WITHOUT SYSTEMS CLARITY

Accountable for AI velocity but blocked by systems complexity. We translate engineering reality into strategic clarity – and close the gap between the two.

Get Started

Start with what's actually wrong.

No RFP. No proposal. A structured diagnostic that tells you exactly where your production AI is losing performance and why.