Production AI, under your control.
Build, deploy, and operate production AI systems across any enterprise environment. Pipelogic helps teams roll out scalable AI solutions wherever the business needs them.
Teams at leading companies trust Pipelogic
AI systems have to fit the environment.
Enterprise AI cannot live only in demos, notebooks, or isolated tools. It has to work with live inputs, private data, existing systems, security requirements, distributed teams, and operational workflows.
Pipelogic helps enterprises move from one-off AI projects to reusable systems that can be governed, deployed, updated, and scaled.
Run where data lives
Deploy backends across cloud, on-premises, edge, or air-gapped environments.
Govern access
Control who can build, release, deploy, inspect, and operate AI systems.
Integrate with existing systems
Connect sensors, APIs, databases, brokers, models, and enterprise platforms.
Scale beyond one pilot
Reuse Applications, Components, Backends, and Runtime patterns across sites.
Audit changes
Track who changed what, when, and why across your AI systems.
Deploy anywhere.
Scale globally in the cloud, or process data securely on local devices.
Public Cloud
Public Cloud — Shared
Fastest way to evaluate Pipelogic, test Applications, and build early Backends.
Public Cloud — Dedicated
Dedicated cloud environment for production workloads that need stronger isolation.
Private Cloud
Private Cloud — Shared Capacity
Single-tenant application environment on managed private cloud infrastructure or customer VPC.
Private Cloud — Dedicated
Dedicated private environment for enterprise production workloads.
On-Premises and Edge
Always-Connected
On-premises / Edge
Run locally with continuous connectivity for monitoring, updates, synchronization, and integrations.
Occasionally Connected
On-premises / Edge
Run locally during normal operation and connect only for installation, updates, synchronization, or deployments.
Air-gapped
On-premises / Edge
Run inside a fully isolated environment with no internet or external network connectivity.
Control how AI systems are built, deployed, and operated.
Pipelogic gives enterprise teams a structured way to manage Applications, Backends, Components, Runtimes, releases, and deployments.
Access control
Manage who can create, edit, release, deploy, view, and operate Pipelogic resources.
Auditability
Track important changes across releases, deployments, configuration, and runtime activity.
Secrets and configuration
Keep credentials, API tokens, endpoints, and environment-specific values separate from code.
Data boundaries
Deploy Backends in the environment that matches your data and infrastructure requirements.
Release control
Use versioned Applications, Backends, and Components instead of unmanaged scripts.
Security review support
Work with Pipelogic on architecture diagrams, data-flow explanations, deployment details, and security review materials.
Turn one successful pilot into a repeatable rollout.
A production AI system should not be rebuilt from scratch for every site, team, or region. Pipelogic helps enterprises reuse the same Application, Backend, Components, and Runtime pattern while adapting local configuration.
Prove the workflow
Start with a working Application and Backend using sample data or a controlled environment.
Pilot with real data
Connect to live streams, private data, local APIs, or site systems.
Standardize the pattern
Create a reusable Backend and deployment blueprint.
Roll out by site or region
Adapt cameras, zones, sensors, users, thresholds, and integrations per location.
Improve continuously
Update Components, models, parameters, Applications, and releases with visibility into what is running.
Connect AI to the systems your enterprise already runs.
Use Pipelogic to connect models, cameras, sensors, APIs, databases, brokers, and business systems into production AI Backends.
Operational systems
MES, ERP, CMMS, WMS, EHS systems, ticketing, reporting, and internal tools.
Physical-world inputs
RTSP cameras, thermal cameras, audio streams, vibration sensors, machine events, labels, and facility metadata.
AI and model infrastructure
Private models, hosted APIs, Triton, TorchServe, vLLM, SGLang, Ollama, ONNX, PyTorch, Hugging Face, and custom inference services.
Data and event systems
HTTP APIs, webhooks, Kafka, RabbitMQ, MQTT, databases, object storage, internal APIs, and cloud services.
User-facing workflows
Dashboards, review queues, control panels, approval flows, customer portals, and internal Applications.
Production AI does not end at launch.
Once a Backend is deployed, enterprise teams need to know what is running, where it is running, which version is active, and how the system is behaving.
Talk to Enterprise SupportDeployment visibility
See active Backends, target Runtimes, deployment status, and environment details.
Runtime health
Monitor whether Runtimes are available and ready to run deployed Backends.
Logs and diagnostics
Inspect logs, runtime output, component behavior, and deployment issues.
Version tracking
Understand which Application, Backend, and Component versions are active.
Parameter updates
Adjust thresholds, filters, destinations, and environment-specific settings.
Enterprise support
Work with Pipelogic on architecture planning, deployment design, troubleshooting, rollout strategy, and production support.
A practical path from review to rollout.
Architecture review
Map your use case, inputs, models, Applications, integrations, deployment environment, and operating requirements.
Security and procurement review
Support internal security, legal, procurement, and platform teams with the information they need to evaluate Pipelogic.
Pilot deployment
Validate the workflow with real data, real users, and a realistic deployment path.
Production plan
Define Runtime sizing, integration requirements, support model, release process, and rollout checklist.
Scale across teams or sites
Reuse the first working pattern across additional locations, teams, regions, or use cases.
Enterprise FAQ
Common questions about running Pipelogic in enterprise environments.
Pipelogic Backends run in public cloud (shared or dedicated), private cloud or your own VPC, on-premises, at the edge, and in fully air-gapped environments. You choose the deployment that matches where your data lives.
You govern who can create, edit, release, deploy, view, and operate Applications, Backends, Components, and Runtimes, and track important changes across releases, deployments, configuration, and runtime activity.
It follows a practical path: architecture review, security and procurement review, a pilot deployment with real data, a production plan, and then scaling the working pattern across teams, sites, or regions.
Yes. Backends can run inside a fully isolated environment with no internet or external network connectivity — suited to sensitive facilities, regulated environments, sovereign infrastructure, and critical operations.
Operational systems like MES, ERP, CMMS, and WMS; physical-world inputs such as RTSP and thermal cameras, audio streams, and sensors; model infrastructure including Triton, vLLM, Ollama, ONNX, PyTorch, and Hugging Face; and data systems like Kafka, RabbitMQ, MQTT, HTTP APIs, databases, and object storage.
Private cloud, on-premises, edge, and air-gapped deployments use a fixed monthly or annual subscription; shared and dedicated public cloud environments are pay-as-you-go.
Bring production AI into your enterprise environment.
Build reusable AI systems that fit your infrastructure, governance, deployment, and operating requirements.