Cloud and AI Infrastructure
GPU bills climb, models stall in staging, and compliance gaps surface the week of audit. Cloud and AI infrastructure that ships to production and stays there.
TRUSTED BY ENTERPRISES
Models stall in staging when cloud architecture, GPU serving, and pipelines get sized for a demo. Diesel Laptops runs 160,000 records in a self-hosted AWS VPC from Kodexo Labs, with 51 products live.
Our Core Capabilities:
Designs production AI architectures sized for real traffic, not demo loads.
Stands up GPU serving and inference layers tuned for cost, latency, and uptime targets.
Deploys self-hosted AI inside the client cloud account when data cannot leave the perimeter.
Pushes inference to the edge for latency-sensitive workloads and disconnected environments.
Builds MLOps pipelines so retraining, monitoring, and rollback run without heroics.
Operates the platform after launch, with response times measured in minutes.
IN THE NEWS
Proof points from across the Kodexo Labs cloud and AI infrastructure practice.
AI-Powered Products Shipped
AI Development Company · Verified on Clutch
Client retention
Team Members · 6 Global Offices
Founded
Teacher AI
One Partner, Five Infrastructure Specialisations Under One Engineering Roof
Kodexo Labs staffs each discipline with senior practitioners who have shipped the same pattern before, then sequences the five so a week-one cloud architecture decision still holds when the MLOps pipeline reaches production.
Cloud Architecture Design Services
Diesel Laptops needed cloud architecture that kept 160,000 records inside the perimeter and still served sub-second AI search to field mechanics, so Kodexo Labs built the VPC.
AWS multi-region designs for AI traffic, with VPC, networking, and IAM patterned on production constraints.
capacity planning before the commit, so the GPU bill matches the forecast.
The Architecture You Choose Today Lasts For Years
Mid-market AI teams call Kodexo Labs before they commit to a cloud stack, because the infrastructure choice locks in cost, latency, and audit exposure long after the first model retires.
Three Industries, Three On-Record Outcomes
Extensiv
Extensiv's operations team waited on engineering for every data question. Kodexo Labs embedded an AI pod that built a LangGraph agentic query layer across 4 databases, so operations self-serves in plain English.
90%
SQL accuracy
207
Tables Accessible
04
Databases
Diesel Laptops (Inc. 5000)
Fleet technicians were spending more time searching repair records than fixing trucks. Kodexo Labs built a self-hosted AI search system on AWS VPC that answers queries across 160,000 records in seconds.
85%
Search Time Reduction
160,000+
Repair Records Indexed
12 Weeks
Build to Production
Vital Connect
Clinical teams missed early-warning patterns in patient data, delaying diagnosis. We built a TensorFlow signal-detection layer that surfaces subtle conditions earlier and accelerates clinical decision-making.
3×
Earlier Detection
40%
Faster Diagnosis
Industry:
Healthcare
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,
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
Cloud And AI Infrastructure Across Eight Industry Verticals
From healthcare and logistics to legal and retail, every industry faces unique infrastructure challenges. Kodexo Labs builds cloud and AI systems tailored to operational, compliance, and performance requirements.
- HIPAA-Compliant AWS VPC for AIBAA-Eligible Service StackPHI Redaction at Inference LayerRole-Based Clinical Access Controls
Your Auditor Will Ask Where The Data Lives. Have The Architecture Diagram Ready
HIPAA, SOC 2, GDPR, and the EU AI Act all land at the infrastructure layer first. Kodexo Labs builds the controls into VPC, IAM, and audit logging on day one, so compliance shows up in the architecture diagram, not in a remediation sprint.
HIPAA, SOC 2, And GDPR Built Into Every Infrastructure Layer
Kodexo Labs designs every cloud and AI infrastructure build for data sovereignty, with self-hosted AI deployment as the default for regulated workloads. SmartMedHx runs 42+ providers inside its own AWS account, where audit logging captures every inference call and BAA coverage spans the full stack.
Why Choose Kodexo Labs For Cloud And AI Infrastructure?
Buyers ask the same four questions on the second call. Below are the answers we put in writing before the engagement starts, so expectations are clear before any infrastructure decisions are made.
Your model will not sit in a notebook.
51 AI-powered products have shipped from notebook to live endpoint on Kodexo Labs infrastructure, with deployment, monitoring, and rollback wired in from the first commit. Logistics, healthcare, and consumer platforms run on the same path: research artifact today, production traffic next quarter.
Your team will not overpay for cloud compute.
GPU bills track to actual inference load, not headroom. Kodexo Labs sizes NVIDIA capacity against measured throughput targets and negotiates reserved pricing before the first cluster spins up. Cost overruns are a planning failure, not an infrastructure property, and the planning ships with the architecture brief.
Your data will not leave your perimeter.
Kodexo Labs deploys models inside the client cloud account on network-isolated VPC architecture. Diesel Laptops (160,000 records, self-hosted AWS VPC) and SmartMedHx (42+ providers, HIPAA-compliant) both run on infrastructure where data never crosses the perimeter and audit logging captures every inference call.
Cloud and AI Infrastructure for Funded Operators
Series B+ operators ship AI on Kodexo Labs infrastructure: Extensiv ($130M+ funded, Hg Capital, Inc. 5000) on agentic data access, Diesel Laptops (Inc. 5000) on self-hosted AWS VPC, and SmartMedHx (42+ providers, HIPAA-compliant, patent-pending AI) on regulated healthcare AI.
Recognised By The Platforms That Vet AI Companies
Kodexo Labs is reviewed where technical buyers do their diligence: Clutch and Upwork. Every badge below links to the live profile.
The Exact Production Stack, Tool By Tool
Kodexo Labs matches the stack to each workload, compliance perimeter, and production SLA, with every tool already running in client builds.
The Cheap Stack Costs More By The Second Load Test
Shortcut architectures look like a budget win until peak traffic hits and the GPU bill, the latency floor, or the compliance gap forces a rebuild. Kodexo Labs builds the perimeter, the pipeline, and the serving layer once, sized for the SLA the board approved.
The Three Failure Modes That Kill AI Projects Between Demo And Production
The model is rarely the problem. Hallucinations surface at scale, data leaves the perimeter, and gaps appear before audit.
Hallucination Control
Agentic queries return wrong answers when grounding is thin. Extensiv holds 90%+ SQL accuracy across 207 tables through a schema-validation layer.
Zero-Trust Data Security
Third-party cloud APIs surrender control fast. Diesel Laptops runs 160,000 records inside a network-isolated AWS VPC with zero egress for security.
Regulatory Compliance by Design
Compliance cannot bolt on late. Pokemon Card processes 260,000+ daily data points with lineage tracking aligned to EU AI Act rules.
The Three Failure Modes That Kill AI Projects Between Demo And Production
The model is rarely the problem. Hallucinations surface at scale, data leaves the perimeter, and gaps appear before audit.
Discovery and Strategy
Kodexo Labs runs a cloud readiness audit, sizes AI workloads against throughput and budget, and maps compliance across HIPAA, GDPR, and the EU AI Act where they apply. The phase closes on the architecture decision, cloud, self-hosted, or hybrid, with Terraform entering scope to scaffold Phase 2. The full audit method lives in the AI Readiness Assessment practice.
Design and Prototyping
Cloud architecture design starts here: VPC layouts, subnet partitioning, IAM scaffolding, GPU cluster shapes, and inference-endpoint topology. Terraform writes the infrastructure-as-code so the build stays reproducible across staging and production. The team writes no application code yet, and every decision lands in the design brief first.
Development and Integration
GPU clusters provision against the Terraform plan. Model-serving endpoints stand up on Triton Inference Server or vLLM by workload shape, with FastAPI fronting the API. Data pipelines wire into Apache Kafka, and the toolchain configures MLflow and Kubeflow, running in staging before production sees a packet.
Deployment and Launch
Production release runs on the staging-validated stack. Load testing pushes the inference layer past expected peak traffic, autoscaling policies bind to real metrics, and monitoring dashboards go live on Weights and Biases and MLflow, with alerts wired to the on-call rotation. Go-live signs off documented.
Support and Optimization
Ongoing model monitoring, cost optimisation, drift detection, and pipeline updates compound on retainer. Vital Connect's signal-monitoring infrastructure now delivers 3x early detection and 40% faster diagnosis, the result of ongoing optimization, not the initial build. The infrastructure keeps getting sharper because someone keeps tuning it.
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Frequently Asked Questions
Cloud and AI infrastructure setup covers GPU provisioning, model serving, data pipelines, and MLOps tooling. Kodexo Labs sizes each build to the client's throughput target, compliance perimeter, and SLA on AWS, Kubernetes, and MLflow before any code ships. The same method has anchored 51 AI-powered products to production.