One platform for building,
running, and governing
AI agents in production.
Each engagement is one agent purpose-built for a specific job. We build it, run it from our infrastructure, govern it against your rules, and surface every output for your approval. What follows is what the platform actually does.
Each agent designed for one specific job.
In your business, against your rules.
Each DeployCo engagement starts with a working session. We define what the agent is for, what it should produce, the systems it can touch, and the boundaries it cannot cross — all encoded at the configuration level, not as aspirational policy documents.
The agent that gets deployed is your agent, configured to your business. Not a template you fill in. Not a generic tool a hundred other companies are also using.
- Specific goals defined in plain language and encoded into the runtime
- Tools the agent can use are explicitly allowed; everything else is blocked
- Voice, tone, and content rules are configured per agent and per use case
- Approval flows mapped to your team structure
Nothing leaves the queue without you.
Every output reviewed before any action is taken.
When the agent produces an output, it lands in your approval queue with full context — what triggered the run, what the agent reasoned, what tools it would have called, and what it produced. Your reviewer approves, edits and approves, or rejects.
No action reaches your customers, your systems, or your money until a human approves it. There is no setting that bypasses this. The queue is the safety layer — a wrong proposal is a non-event, because nothing the agent produces executes until you say so.
- Full execution context attached to every output
- Approve, edit and approve, or reject — three actions, no friction
- Multi-step approval flows for sensitive use cases
- Per-team queue routing based on output type and content
Rules enforced before generation.
Not a filter at the end. A gate at the start.
The agent operates inside a policy stack defined for your business. Organizational rules, agent-specific constraints, and execution-time policies all enforce what the agent can do — and what it cannot.
Constraints are checked before an action is generated, not after. If a tool isn't allowed, the agent doesn't reach for it. If a topic is prohibited, the content isn't produced. Governance is structural, not advisory.
- Allowed and blocked tool lists enforced per agent
- Topic and content rules configured per business unit
- Risk-tiered actions with optional human review
- Role-based access boundaries across your organization
Every action logged.
Append-only. Reconstructable months later.
Every agent run, every reasoning step, every tool call, every approval decision, and every admin action is written to an append-only audit log. The log is yours; it is not edited, redacted, or quietly cleaned up.
When your compliance team or your customer asks what happened on a specific day at a specific time, the answer is in the log. No reconstruction from memory. No guessing.
- Append-only log retained for the life of the engagement
- Per-event metadata: actor, action, target, timestamp, context
- Searchable and filterable by date range, agent, or actor
- Exportable for SIEM ingestion or compliance review
Connected to where the work already happens.
Email, CRM, support, publishing platforms, internal APIs.
The agent does not live inside a chat window. It connects to your existing systems — sending email through your inbox, updating records in your CRM, posting to your publishing channels, calling your internal APIs.
The integrations your engagement needs are built as part of the engagement. If your stack has an API, the agent can connect to it. If it doesn't, we build a webhook bridge.
- Email — Gmail, Outlook (OAuth-authenticated)
- CRM — HubSpot, Salesforce, custom data flows
- Support — Zendesk, Intercom, ticket routing
- Publishing — LinkedIn, Medium, Beehiiv, scheduled posting
- Internal — any HTTP endpoint with allowlisted domains
We run it. You review it.
The runtime, monitoring, and ongoing improvement are ours.
The agent runs on DeployCo infrastructure, not yours. We handle the runtime, the monitoring, the model upgrades, the integration maintenance, the drift detection. Your team reviews work — that is the entire relationship.
When something goes wrong — a model behaves unexpectedly, an integration breaks, a rule fires that shouldn't have — we see it before you do. When the rules need to change because your business changed, we make the changes and walk you through them.
- Cloud runtime — no infrastructure to provision on your side
- Continuous monitoring for drift and operational anomalies
- Model upgrades and integration maintenance handled by our team
- Rule updates and policy adjustments as your business evolves
See what the agent did, and where it needed you most.
Regular reporting on outputs, approvals, and where judgment was applied.
Each engagement includes regular operational reporting: how many outputs the agent produced, how many were approved as-is, how many were edited before approval, how many were rejected, and where in the work the agent needed your team's judgment most.
The point isn't metrics for their own sake. It is visibility into what the agent is doing well, where it is struggling, and what to refine next.
- Output volume and approval rates by week and by agent
- Edit patterns — where your team is correcting the agent most
- Rejection reasons — what categories of output are not landing
- Operational summary delivered on a regular cadence
See how the platform fits your business.
Tell us what you would want an agent to do. We will walk you through what that engagement would look like specifically for your team.