AI Infrastructure Services
Capacity guessed from demo load is what turns a launch into runaway cost. AI infrastructure services provision GPU fleets, tune the serving stack, and size capacity to measured traffic. Kodexo Labs, an AI infrastructure company, ships this across 51 products and 25+ industries.
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TRUSTED BY ENTERPRISES
Most models die in the gap between a clean staging demo and real production load. We provision the GPU fleet, stand up Triton and vLLM serving, and wire observability in from day one.
Our Core Capabilities
GPU fleet provisioning across NVIDIA A100 and H100, fully Kubernetes-orchestrated.
Model serving tuned on Triton Inference Server and vLLM engines.
Capacity planning sized to measured production traffic, not demo load.
Autoscaling policies that follow real, variable AI workload traffic patterns.
Observability for latency, cost, and GPU utilization wired in early.
Multi-cloud GPU portability across AWS, Google Cloud, and Azure, Terraform-managed.
IN THE NEWS
AI-powered products
AI development company
Expert team
Client retention rate
AI Infrastructure Services and GPU Capabilities
Most teams don't need one giant cluster. They need GPUs provisioned right, a serving stack that holds under load, and capacity sized to real traffic. Here's how we build out each layer of your AI infrastructure.
GPU Fleet Provisioning & Orchestration
Spinning up GPUs by hand doesn't scale past a few nodes. We provision NVIDIA A100 and H100 fleets, orchestrated on Kubernetes and right-sized.
We pool, schedule, and share nodes so no costly GPU card ever sits idle.
Nodes join a shared pool, get health checks, and then scale up or down.
Not sure how much GPU capacity you need?
Tell us your real traffic and latency target. We'll model the GPU capacity and serving stack you actually need before any build begins.
Infrastructure Proof in Production
Diesel Laptops
Fleet technicians at Diesel Laptops burned more time hunting diagnostic parts records than actually fixing trucks, and every idle minute meant a truck sitting off the road. We stood up an AI serving stack self-hosted inside the client's own AWS VPC, searching 160,000+ technical records in seconds flat. Parts lookup ran 85% faster, all inside an Inc. 5000 business.
85%
Faster Lookup
160,000
Records Searched
AWS VPC
Self-Hosted
Extensiv
Extensiv's operations teams needed answers from 207 tables across four databases, but nobody could write SQL fast enough to keep up with them. We built a LangGraph agentic system on serving infrastructure that turns plain-English questions into queries at over 90% accuracy. The Hg Capital-backed, Inc. 5000 logistics platform rated the whole engagement a perfect 5.0 score on Clutch.
90%+
SQL Accuracy
207
Tables
04
Databases
SmartMedHx
SmartMedHx handles sensitive interview data from 42+ providers, so patient records could never sit on shared, unisolated infrastructure for even a moment. We architected HIPAA-compliant serving from day one, with patient-data isolation and audit-logged inference built into the stack rather than bolted on later.
42+
Providers
493
Patient Interviews
HIPAA
Compliant
What Clients Say About The Team
Fast-growing organisations do not applaud a consulting partner for polished slide presentations; they praise it for showing up when something actually breaks. The notes below come from founders who watched Kodexo Labs work the problem in real time.
Kodexo Labs has met all expectations; the team delivers on time and manages the project seamlessly. They respond promptly to needs and communicate effectively through virtual meetings, Google Chat, and WhatsApp. Overall, they're highly passionate about the project and excel in customer service.
Christopher Brigham
MD President, Brigham and Associates, Inc.
WATCH VIDEO
- HIPAA-architected infrastructurePatient-data isolationAudit-logged inferenceEncrypted serving stack
Your Industry Sets the Compliance Floor for AI infrastructure
A hospital and a warehouse never carry the same data-isolation or latency demands, so we architect GPU serving to each vertical's real constraints. We build across seven regulated industries below. BPO and Contact Center stays on hold.
Still sizing GPU capacity off a demo, not real traffic?
Send us your traffic pattern and latency target, and we'll model the GPU fleet, serving stack, and monthly cost before any commitment. You approve the plan first, then we build against measured numbers.
Auditors Ask for Proof Before Your AI infrastructure Ships
A control you can't evidence is a control you don't have, so we wire compliance into the serving stack from day one rather than bolting it on later. These frameworks shape how we isolate data, log inference, and pass every compliance audit without surprises.
HIPAA
SOC 2 Type II
GDPR
CCPA
AWS Shared Responsibility Model
ISO 27001
PCI-DSS
NIST AI RMF
FERPA
COPPA
HIPAA
SOC 2 Type II
GDPR
CCPA
AWS Shared Responsibility Model
ISO 27001
PCI-DSS
NIST AI RMF
FERPA
COPPA
Choosing an Infrastructure Partner is a Bet on Whether the GPU Numbers Hold
Most GPU builds look fine in a pitch deck and break under real traffic. Here's what separates ours: capacity we can defend, a serving stack proven in production, and a team that owns every number.
Capacity Modeled, Not Guessed
Most GPU bills balloon because capacity got sized off a demo, not real load. We model your traffic first, plan headroom, then show monthly spend per scenario before a single node spins up.
Serving Stack Proven Live
A model that is fast in a notebook can crawl once real traffic lands. We serve on Triton, vLLM, and Kubernetes, the same LangGraph agentic pattern that runs behind Extensiv's system in production.
Self-Hosted in Your VPC
When records can't sit on shared infrastructure, a public endpoint is out. We serve self-hosted in your own AWS VPC, the same pattern behind Diesel Laptops' production search, so your data stays put.
Led by PhD-Level Engineers
GPU decisions are hard to reverse once production traffic depends on them. Our work here is led by Syed Umaid Ahmed, PhD Scholar at FAST-NUCES, Lead ML Engineer, and Microsoft Certified BI Analyst.
Paying For GPU Capacity That Sits Idle?
Idle GPUs bill by the hour, and oversized fleets quietly drain your budget. Send your traffic pattern and latency target, we'll model the right GPU capacity, stack, and autoscaling policy, and show you projected spend before you approve a thing.
Before You Shortlist
Independent review sites carry more weight than any claim we could make. Clutch and Upwork rank us on verified client reviews, not marketing, across AI and machine learning.
Overcoming AI Infrastructure Services Challenges
Most AI infrastructure projects don't fail on the model. They fail on the plumbing underneath it. Below are the five failures we watch teams hit most, and the way we architect each one out before your production build ever ships.
Every tool listed is in active production on a Kodexo Labs.
Every framework, runtime, and cloud service named here is running on a live client product right now. No theoretical stack, no resume keywords, no tools added for marketing weight.
How We Stand Up Your Production Infrastructure
Discovery & Capacity Audit
We start with your real numbers: current traffic, latency targets, model size, and compliance obligations. No GPU gets sized off a demo. We audit what you run today and map where production load will actually land.
Architecture Design & Cost Modeling
Next we design the GPU fleet and serving architecture, then model spend per traffic scenario. You see projected cost before any commitment, so the budget holds and headroom gets planned deliberately rather than padded.
GPU Provisioning & Serving Stack Build
We provision NVIDIA A100 and H100 nodes on Kubernetes, then stand up the serving stack on Triton and vLLM. Autoscaling policies follow real demand, so capacity tracks live traffic instead of guesswork or padding.
Load Testing & Observability Wiring
Before launch we load test against real traffic, not a single-user demo. We wire latency, cost, and GPU utilization metrics into the stack, with alerts that page you before a slow model becomes an outage.
Production Handoff & Monitoring
At handoff you get live dashboards, runbooks, and a monitoring setup your own team can operate. We stay on to watch utilization and cost, tuning capacity as your real production traffic grows.
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Frequently Asked Questions
AI infrastructure services provision GPU fleets, build the model-serving stack, and size capacity to measured production traffic rather than demo load.