Average enterprise AI spend, with pressure increasing as pilots move into production.
AI infrastructure optimization
Optimize the cost and performance of your production AI infrastructure.
cogniware.ai's middleware platform raises GPU utilization from 15-30% to 60-80%+, reducing AI infrastructure costs by up to 70% for suitable workloads.
A note on the name: cogniware.ai, our infrastructure partner, and cogniware.com, an AI investigation platform, are two separate companies.
The problem
AI infrastructure spend is rising, but much of the capacity is idle.
Typical GPU utilization before operational optimization. 60-80% is achievable for suitable workloads.
Organizations now actively managing AI spend as infrastructure becomes a board-level cost topic.
Sub-50% GPU utilization usually indicates recoverable cost and performance opportunity.
What cogniware.ai does
Four layers of AI infrastructure optimization.
cogniware.ai applies 10 optimization levers across the model, runtime, accelerator, and facility layers. The approach is hardware-agnostic across NVIDIA, AMD, and Intel environments.
Model layer
Align model selection, sizing, batching, precision, and serving patterns to workload requirements.
Runtime layer
Improve scheduling, routing, orchestration, queueing, and workload placement across inference demand.
Accelerator layer
Raise utilization across GPU and accelerator pools while reducing idle capacity and avoidable contention.
Facility layer
Connect infrastructure decisions to power, cooling, location, and sovereign deployment constraints.
10 levers
Use a practical lever model to identify where cost, latency, throughput, and resilience can improve.
Hardware-agnostic
Support optimization across mixed infrastructure estates instead of locking every decision to one accelerator vendor.
Methodology
A four-step path from waste discovery to governed production.
Baseline
Measure spend, utilization, workload patterns, latency, throughput, and infrastructure constraints.
Route
Map workloads to the right infrastructure paths based on cost, performance, sovereignty, and reliability needs.
Optimize
Apply model, runtime, accelerator, and facility levers against prioritized production workloads.
Govern
Put controls in place so cost, utilization, and deployment posture remain visible after implementation.
GCC sovereign AI
Optimization must respect regional data and deployment constraints.
Saudi PDPL requirements
Architect AI deployments with data residency, transfer, access, and processing obligations in view from the start.
UAE privacy law + AI Strategy 2031
Balance AI adoption goals with privacy, hosting, operating model, and governance expectations.
Across GCC
A 305% rise in sovereignty inquiries shows that infrastructure location and control are now core AI design decisions.
in-box.ai role
What in-box.ai adds.
Assessment
Identify suitable workloads, cost drivers, utilization gaps, sovereignty constraints, and measurable target outcomes.
Architecture
Design the deployment pattern, integration model, hosting posture, and controls needed for production use.
Implementation
Coordinate platform rollout, telemetry, workload routing, optimization steps, and acceptance criteria.
Governance
Establish ongoing cost, performance, access, compliance, and operational review practices.
Separate offering under review
AI for investigation and discovery - a separate offering we are exploring.
cogniware.com is a Czech AI investigation platform company, separate from cogniware.ai. Its Argos and Explorer products support AI-powered investigation and evidence search, including IBM watsonx integration. This relationship is not yet formalized.
FAQ
Common AI infrastructure questions.
Which workloads are suitable for optimization?
Production inference, repeated batch inference, and GPU-backed AI services with measurable utilization, latency, and cost patterns are the best starting points.
Does this replace our cloud or GPU provider?
No. The work focuses on middleware, routing, operations, and architecture so existing infrastructure can be used more efficiently.
How quickly can savings be confirmed?
A baseline assessment can usually identify the size of the opportunity first. Implementation timing depends on access, workload complexity, and governance requirements.
Can this support sovereign AI deployments?
Architecture decisions can account for GCC residency, privacy, hosting, access, and audit requirements before optimization is applied.
Find the recoverable cost in your AI infrastructure.
We will baseline workloads, utilization, sovereignty needs, and practical optimization routes.