faq
deployed.engineering builds production-ready vertical AI agents for industry-specific workflows, integrations, security boundaries, and ongoing operational improvement.
deployed.engineering is a forward deployed engineering company that builds production-ready vertical AI agents for specific industries and operational workflows. The team maps customer operations, connects tools, deploys agents, and keeps improving them after launch.
deployed.engineering builds custom vertical AI agents, deployment infrastructure, workflow automation, and product spinouts. Current focus areas include real estate brokerage operations, security staff management, hosted agents, messaging channels, and secure production agent runtime.
deployed.engineering is for founders, startups, and operating teams that need AI agents to execute real business workflows. It is best suited for teams with repeatable operational work, tool handoffs, domain-specific context, and a need for production ownership.
A vertical AI agent is an AI system built for a specific industry, role, or workflow instead of a general chat use case. deployed.engineering designs vertical agents around customer data, domain rules, integrations, permissions, and operational feedback loops.
CompanyOS is deployed.engineering's operating-layer framing for production vertical AI agents. It treats the model as one layer inside a system of workflow truth, domain memory, tool access, policies, approvals, feedback loops, and production ownership.
deployed.engineering starts by mapping the workflow with the operators who run it, then designs the agent, connects the required tools, deploys it into production, and iterates based on real usage. The engagement is closer to forward deployed engineering than a handoff-only software project.
deployed.engineering focuses on production vertical agents that execute operational workflows, not generic chatbot demos. The work includes domain modeling, integrations, deployment, security boundaries, runtime reliability, and ongoing improvement after the agent is live.
Yes. deployed.engineering routes agent work across frontier model stacks while keeping customers insulated from model churn. The model is treated as one layer of the system, alongside workflow context, tools, security, and deployment infrastructure.
Yes. deployed.engineering is developing a Real Estate Broker Agent for lead response, listing context, buyer qualification, follow-up, and deal-room coordination. The agent is designed around brokerage operations rather than generic sales automation.
Yes. deployed.engineering is developing a Security Staff Management Agent for guard scheduling, incident capture, site instructions, escalation routing, and compliance-ready operational memory. The agent is designed for security staff management workflows.
Yes. deployed.engineering can build AI agents for internal operations where work depends on context, exceptions, permissions, handoffs, and tool coordination. Strong fits include customer operations, research, support, sales follow-up, back-office workflows, and field operations.
deployed.engineering designs production agent deployments around isolated runtimes, encrypted secrets, scoped access, separated state, and operational review. Security controls are mapped to each customer workflow before production rollout.
deployed.engineering designs each production deployment around a separate runtime boundary, with storage and database boundaries instead of a shared namespace. The goal is to keep credentials, state, and operational memory separated per deployment.
deployed.engineering encrypts API keys, bot tokens, and integration credentials before storage and only decrypts them for deployment or runtime operations where the credential is needed. Secret handling is part of the deployment review.
The first step is to book a meeting and bring one hard workflow that would be valuable to automate. deployed.engineering will map the operation, identify integrations and security boundaries, and define the first production agent deployment path.
Yes. deployed.engineering stays involved through deployment, feedback, integrations, reliability work, and iteration. The operating model is designed for production ownership rather than a prototype-only handoff.
start with one workflow
The first step is a working session to map the process, integrations, security constraints, and production deployment path.
