Description
Plexe is an applied AI lab: a forward-deployed engineering team paired with a platform for building production machine learning models and AI agents. Instead of selling seat-based software, Plexe's senior engineers work inside a customer's own product and stay until the system is live, typically shipping in weeks rather than quarters.
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The underlying platform has three parts. Builder turns a plain-language description of a problem into a production model, generating, evaluating and benchmarking multiple approaches in parallel. Agents compose AI agents that read, write and act across a company's own tools and data, with guardrails and policy controls built in. Runtime deploys the resulting model or agent, A/B tests it against whatever it is replacing, monitors for drift, and retrains it as needed. Underneath these sit fifteen production capabilities, including an AI agent runtime, predictive ML serving, a RAG and vector store, document understanding, a workflow and DAG engine, an eval framework, and observability and monitoring.
Everything Plexe builds is designed to be self-hosted inside the customer's own VPC, or fully managed if preferred, and the runtime is sealed and containerized so Plexe has no access to the customer's source code, weights or prompts. Plexe is SOC 2 Type 2 audited. The customer keeps the solution code, configurations, prompts, business rules and data, and can invoke a "graduation right" to take any solution fully in-house at any time, with a complete handover.
Plexe does not publish fixed software tiers. Engagements are scoped around the workflow, integration depth and support level a project needs; starting points include a scoped pilot on a single workflow, a full custom build of a customer-facing agent, or an embedded team that keeps building after launch. One documented customer, a Fortune 500 travel company, now runs six production models through Plexe, operated by non-technical staff, with a measured 7% conversion uplift.
Plexe is built for companies that want production ML and AI agents without building an in-house ML team from scratch. Its own team includes engineers and data scientists from Imperial College, Oxford, AWS and Expedia, and it is backed by Y Combinator, with operators from Microsoft and Shopify among its backers. Plexe reports more than 30 production deployments and millions of inferences served daily. Because Plexe is a consulting-led engagement rather than a self-serve tool, it will not suit teams looking to start building with no sales conversation.
- CategoryMachine Learning Platforms
- Pricing ModelContact sales
- DeploymentAPI, Batch Jobs
- PlatformsWeb
Plexe's Core Features
Builder turns a plain-language problem description into a production ML model, benchmarking multiple approaches in parallel
Agents that read, write and act across a company's own tools and data, with guardrails and policy controls
Runtime that deploys models, A/B tests them, monitors drift and retrains on the fly
Self-hosted deployment inside the customer's own VPC, or fully managed
SOC 2 Type 2 audited, sealed containerized runtime with no Plexe access to source, weights or prompts
Full code and model ownership, with a graduation right to bring any solution in-house
Built-in RAG and vector store, document understanding and an evaluation framework
A senior engineering team embedded with the customer's own team through delivery
How to use Plexe?
Scope: Talk to the Plexe team about the workflow, data and production outcome you need.
Pick an engagement: start with a scoped pilot, a full custom build, or an embedded team.
Build: Plexe's engineers design, integrate and evaluate the model or agent inside your stack.
Deploy: ship to production, self-hosted in your VPC or fully managed by Plexe.
Monitor and retrain: track performance and drift through Runtime, and retrain as usage changes.
Take ownership: use the graduation right to move the solution fully in-house with a handover.
Plexe's Use Cases
- Production ML model building
- AI agents over internal tools
- Self-hosted model deployment
- Embedded engineering delivery
