Enterprise AI Deployment Infrastructure

FROM MODEL
TO MISSION-CRITICAL

We take your GenAI and machine learning models from prototype into production — reliably, securely, and at enterprise scale.

99.9% Uptime SLA
<200ms Inference Latency
SOC 2 Compliant
LIVE INFERENCE
MULTI-CLOUD
ENTERPRISE-GRADE
GenAI Integration MLOps Pipelines Model Monitoring LLM Deployment Vector Databases RAG Architectures Fine-Tuning Services Inference Optimization AI Governance Zero-Downtime Deploy GenAI Integration MLOps Pipelines Model Monitoring LLM Deployment Vector Databases RAG Architectures Fine-Tuning Services Inference Optimization AI Governance Zero-Downtime Deploy
01 — What We Do

Three pillars.
One platform.

Most AI projects fail in production. We exist to change that — with infrastructure, tooling, and expertise that makes enterprise AI reliable from day one.

SVC / 001

GenAI Integration

Weave LLMs, multimodal models, and generative AI into your existing enterprise applications — with the reliability your business demands.

LLM APIs RAG Fine-Tuning Agents Prompt Ops
SVC / 002

MLOps Platform

End-to-end machine learning operations: continuous training, model versioning, drift detection, and governance at every layer of the stack.

CI/CD for ML Monitoring Drift Detection Model Registry
SVC / 003

Enterprise Deployment

Cloud, on-premise, or hybrid. We handle the infrastructure complexity so your AI applications run where they need to, how they need to.

AWS / GCP / Azure On-Prem Kubernetes Security
02 — Deployment Pipeline

Signal-to-production
in four steps.

01

Assess & Architect

We audit your existing models and systems, map integration points, and design a deployment architecture built for your scale and constraints.

02

Build & Integrate

APIs, pipelines, embeddings, vector stores — we wire your AI into your existing stack with surgical precision and zero disruption.

03

Deploy & Harden

Containerized, monitored, secured. We deploy to your infrastructure with full observability and a hardened security posture from day one.

04

Monitor & Scale

Drift alerts, performance dashboards, and continuous optimization. We keep your models accurate and your systems healthy as you grow.

03 — Why ObsygnalAI

Built for the
enterprise signal.

Production-first, not prototype-first

We design every AI system with production constraints in mind: latency budgets, security posture, audit trails, and zero-downtime deployments — from the first line of code.

Full-stack AI depth

From model selection and fine-tuning to Kubernetes orchestration and real-time inference — we cover the full vertical of the AI deployment stack with specialists at every layer.

Embedded, not outsourced

We work inside your teams, understand your domain and data, and build AI systems that genuinely fit your enterprise context — not generic templates applied at arm's length.

obsygnal-deploy v2.4.1 — prod-us-east
$obsygnal deploy --env prod --model rag-v3
Authenticating to registry... OK
Loading model artifacts [312MB]...
Running pre-flight checks...
✓ Security scan passed
✓ Schema validation passed
✓ Latency baseline: 142ms
Deploying to cluster...
Scaling to 8 replicas...
✓ Health checks (8/8) passed
✓ Traffic shifted → v3 (canary 10%)
⚠ Drift detection ACTIVE — watching
✓ Deployment complete. 0 downtime.
$_
Inference P95
142ms
Uptime (30d)
99.97%
Requests / min
48.2K
Drift Score
0.02
04 — Technology

The stack behind
every deployment.

PyTorch
TensorFlow
HuggingFace
LangChain
LlamaIndex
OpenAI / Azure OpenAI
Anthropic Claude
MLflow
Weights & Biases
Kubernetes
Docker
Ray Serve
Triton Inference Server
Pinecone
Weaviate
Apache Kafka
Apache Airflow
AWS / GCP / Azure
Prometheus
Grafana
dbt
Terraform
Ready to Deploy

Your AI deserves
to go live.

Stop leaving intelligence on the table. Let's take your AI to production.