Too many tools, no platform
Models, agents, data, GPU infrastructure, and governance evolve as separate projects with no stable path to production.
AI-native platform & infrastructure consulting
AI-native platforms.
Built for production.
We help engineering teams design, build, and operate the secure infrastructure behind AI products, LLM inference, and agentic systems. In your cloud, with your team, and fully owned by you.
Senior engineering Open architecture No vendor lock-in
EXPERIENCE
AI products Agents Internal tools
CONTROL PLANE
AI gateway & orchestration Identity / policy / routing / tracing
policy active AI RUNTIME
01Inference
vLLM / KServe
02Agent runtime
MCP / workflows
03Knowledge
Vector / data
GITOPSSynced
GPU FLEETAuto-scaling
OBSERVABILITYTraces + metrics
SECURITYPolicy enforced
26 technologies / open ecosystem
Engineering for the stack you run
- AI & ML Tooling AIBrix Stack
- AI & ML Tooling KGateway & Gateway API Inference Extension
- AI & ML Tooling LangGraph
- AI & ML Tooling Qdrant Vector Database
- AI & ML Tooling vLLM Production Stack
- Automation CI/CD & GitOps Automation
- Automation Terraform by HashiCorp
- Cloud Provider Google Cloud Platform (GCP)
- Kubernetes Amazon EKS
- Kubernetes Google Kubernetes Engine (GKE)
- Observability Langfuse
- Observability Prometheus-Grafana Stack
- Runtime Docker
- AI & ML Tooling Hugging Face
- AI & ML Tooling KServe
- AI & ML Tooling NVIDIA Dynamo Platform
- AI & ML Tooling Vertex AI
- Automation Argo CD & GitOps Workflows
- Automation Helm
- Cloud Provider Amazon Web Services (AWS)
- Cloud Provider Microsoft Azure
- Kubernetes Azure Kubernetes Service (AKS)
- Kubernetes Karpenter
- Observability OpenTelemetry
- Orchestration Kubernetes
- Security & Compliance Automated Security
Inside the platform
See the systems.
Not just the promise.
Architecture, delivery, and operations stay inspectable. These are the kinds of artifacts and live signals we build around every production AI platform.
Telemetry active
LIVE OPERATIONS / GRAFANA
Production inference, visible in real time
Latency, throughput, token volume, and runtime health stay measurable from the first production workload.
Explore production operations →
Inspectable system
REFERENCE ARCHITECTURE / AGENTS
An open runtime for multi-agent systems
A concrete view of orchestration, tools, data, policy, observability, and self-hosted model serving.
Inspect the architecture →
Inspectable system
DEPLOYMENT BLUEPRINT / KSERVE
A governed path from Git to GPU
Declarative model serving with an AI gateway, autoscaling, caching, and heterogeneous GPU capacity.
Read the deployment guide →The production gap
AI demos are easy.
Operating AI is systems engineering.
The hard part starts after the model works: making it secure, reliable, observable, cost-aware, and simple enough for product teams to use.
Pilots skip production concerns
Security, tenancy, evaluations, observability, and incident response arrive late, when the architecture is hardest to change.
Scale makes inefficiency visible
Idle GPUs, slow model starts, duplicated stacks, and opaque API spend turn adoption into an infrastructure cost problem.
+
The target: one paved path from idea to operated AI, without giving up control of your data, cloud, or architecture.
Core capabilities
From platform decision
to production operation.
We combine advisory with hands-on implementation. The architecture is not handed over as a deck; it is proven in your environment on a real workload.
01
AI platform strategy & architecture
Turn an AI roadmap into a platform plan your engineering and security teams can execute.
- Platform readiness assessment
- Target architecture and delivery roadmap
- Build, buy, and model-serving economics
02
Production AI infrastructure
Build the secure cloud foundation that takes AI workloads from a promising pilot to an operated service.
- GPU Kubernetes and workload isolation
- Infrastructure as Code, GitOps, and CI/CD
- Identity, policy, secrets, and observability
03
LLM inference & agent platforms
Give product teams a governed, high-performance runtime for models, agents, tools, and enterprise data.
- Multi-model gateways and OpenAI-compatible APIs
- vLLM, KServe, autoscaling, and model caching
- Agent runtimes, MCP gateways, and tracing
04
Reliability, FinOps & enablement
Make the platform measurable, cost-aware, and operable by the team that will own it after delivery.
- SLOs, runbooks, dashboards, and incident readiness
- GPU utilization and inference cost optimization
- Embedded delivery, training, and handover
How we work
De-risk first.
Build second. Transfer always.
A staged engagement keeps the first decision small and makes each next investment depend on evidence from your systems, not a generic transformation template.
-
Find the real constraint
We examine workloads, architecture, delivery flow, security, and economics before prescribing technology.
OUTPUT / Readiness brief -
Design the smallest viable platform
We define the target state, decision records, operating model, and a sequenced path to production.
OUTPUT / Architecture + roadmap -
Prove it on a real workload
Our engineers build alongside yours and take one production use case through the complete platform path.
OUTPUT / Production platform slice -
Transfer ownership, then expand
We automate operations, document the system, train your team, and create repeatable onboarding patterns.
OUTPUT / Owned operating model
Selected work / Property intelligence
From fragmented property data to a production AI platform.
The six-month engagement produced a platform that now processes one million data events daily across self-hosted models, training pipelines, and dynamically routed LLM services.
Read the case study 1 million Data events processed daily
Around 20 Self-hosted ML and LLM models
10 Production training pipelines
30x Observed acceleration with the GPU deployment
Open engineering
Inspect the thinking
before you hire us.
We publish reference architectures and reusable infrastructure modules because good consulting should leave clients with more ownership, not more dependency.
REFERENCE ARCHITECTURE / 01
Production AI Platform Architecture
A layer-by-layer blueprint for secure, scalable, observable AI infrastructure across cloud providers.
Explore architecture → REFERENCE ARCHITECTURE / 02
Open-source multi-agent systems
The runtime, data, control-plane, security, and observability patterns behind production agent platforms.
Read the field guide → OPEN SOURCE / GITHUB
EKS vLLM Production Stack
A deployable Terraform blueprint for production-grade vLLM inference on Amazon EKS.
View repository ↗
FOUNDER / PRINCIPAL ENGINEER Principal-led delivery
The people shaping the architecture stay close to the implementation.
Drizzle was founded by Aymen Segni after more than 12 years building and operating distributed platforms across SRE, DevOps, cloud, and AI infrastructure.
01Practitioners, not slideware
Recommendations are grounded in operating systems under real reliability and cost constraints.
02Work with, not around, your team
Decisions, code, and operational knowledge stay visible throughout delivery.
03Ownership is a deliverable
Your cloud account, repositories, runbooks, and team remain in control.
Field notes
Engineering guidance
from the production edge.
MODEL INFERENCE / 2026 FIELD REPORT
Browse all insights The serving unit is no longer a replica
A current map of execution engines, distributed runtimes, KV state, routing, gateways, and what is ready for production.
Read the field report → AI PLATFORMS / ARCHITECTURE THESISAI changes the unit of operation
Why production AI must be operated as a behavioral outcome, with a pattern language grounded in real platform work.
Read the platform thesis → AGENT PLATFORMS / ARCHITECTUREMulti-agent systems in production
The control plane, runtime, data, security, and observability patterns for open, governed agent platforms.
Explore architecture →Common questions
Before we build anything.
Clear answers about scope, ownership, and how an AI platform engagement begins.
01 Do we need a new platform before we can launch an AI product?
Usually not. We start with the smallest platform slice needed for one production workload, use your existing cloud and delivery stack where it is sound, and expand only when the workload proves the need.
02 Can you work with our existing platform team?
Yes. Embedded delivery is the default. We make architecture and implementation decisions with your engineers, leave the system in your repositories and cloud accounts, and transfer the operational knowledge as we build.
03 Are you tied to a cloud provider or model vendor?
No. We design around workload requirements, security constraints, and economics. The resulting platform uses open interfaces and replaceable components so models, runtimes, and cloud services can evolve without a full rebuild.
04 Where does an engagement start?
With a focused engineering conversation and a platform assessment. We identify the highest-risk assumptions, define the first production outcome, and give you a practical next step before proposing a larger programme.
05 Do you stay involved after launch?
When useful, yes. We can support reliability, cost optimization, upgrades, and new workload onboarding. The platform is still designed so your team can operate it independently rather than depend on us.
Start with the real constraint
Your AI roadmap is moving.
Is your platform ready?
Bring us the workload, the architecture, or the bottleneck. In one engineering conversation, we will identify the most useful next step.
Book a platform conversation No sales handoff. Speak directly with an engineer.