The Operating System for
Your AI Workforce
Design business automations visually or describe them in conversation. Click Run. You're in production — with monitoring, cost tracking, access control, and audit trails from the start.
Building an agent — agentically
Build AI Workers, Not Scripts
Doozer AI Workers are specialists — each configured with a role, a curated set of tools, domain knowledge, and persistent memory. A support worker knows your product, connects to your CRM, remembers customer patterns, and escalates by your rules. A compliance worker knows the regulatory framework, extracts and classifies documents, and flags exceptions for human review.
Workers aren't chatbots. They're team members with access to company systems, context, and a defined job.
How Teams Use Doozer
From claims triage to employee onboarding — teams deploy AI workers that act, decide, and escalate across real systems in days, not months.
Automated Claims Triage
An agent monitors incoming claims via email, extracts key details from attached documents, checks policy rules and fraud indicators, then auto-approves straightforward cases by updating the claims system — while routing high-risk cases to an adjuster's task inbox with SLA-tracked human-in-the-loop approval.
Contract Lifecycle Automation
A legal ops team deploys an agent that ingests new contracts on arrival, extracts key terms and obligation dates, flags non-standard clauses for attorney review via human-in-the-loop, and writes approved terms back to the CLM system — with escalation if review deadlines pass.
Lead Qualification Pipeline
A B2B sales team deploys an agent that pulls new leads from HubSpot, enriches them with firmographic data via API, scores fit and intent with an LLM call, creates a personalized outreach draft in the CRM, and routes hot leads to the right rep in Slack — cold leads get a nurture sequence automatically.
Employee Onboarding Orchestrator
An HR team deploys an onboarding agent triggered by a new hire record in the HRIS. The agent provisions accounts across IT systems, sends welcome emails with benefits enrollment links, schedules orientation sessions, and requests manager approval for equipment — completing a 12-step checklist that used to take three departments a full week.
Two Ways to Build. Zero Infrastructure.
Design Visually
Drag steps onto a canvas. Connect them. Configure branching, loops, parallel execution, human approval gates, and sub-workflows. 22 step types across 6 categories. Run directly from the designer and watch execution in real time.
Describe What You Want
Doozer's Build Mode is an AI agent — powered by Claude Opus — that discovers the platform's APIs, creates tools, configures workers, assembles workflows, and tests them. All through conversation.
No servers. No deployment pipeline. No ops team required.
How Work Starts
Schedules
Run daily, weekly, or on any cron schedule with timezone support.
"Generate a weekly pipeline report every Monday at 8am"
Gmail, IMAP, or Microsoft 365 — filter by label, folder, or sender.
"Process incoming invoices the moment they hit your inbox"
Forms
Shareable public URLs that feed data straight into a workflow.
"Clients submit onboarding docs through a shared link"
Chat
A message in Teams or Slack kicks off a workflow instantly.
"Sales rep types '/qualify Acme Corp' in Slack"
Webhooks
Events from Stripe, GitHub, Jira, or any system that sends HTTP.
"New Stripe payment triggers fulfillment automatically"
Files
A new file lands in storage and the workflow starts automatically.
"Uploaded contracts get extracted and classified on arrival"
API Polling
Watch any API for changes on a timer — no webhook required.
"Check your vendor portal for new orders every 15 minutes"
Your Agent Never Clocks Out
Configure your workers to stay active — monitoring inboxes, checking systems, and acting the moment something needs attention. No shifts. No overtime. No missed opportunities.
What Makes an AI Worker Effective
Every AI Worker is configured with four things. Build once, share across every workflow.
Persona
A system prompt that defines the worker's role, personality, and behavioral guidelines. A support worker and a compliance worker interpret the same data differently, because they have different mandates.
Curated Toolset
Not every tool in the system — the specific tools this worker needs. Fewer tools means faster, more accurate tool selection. Scoping tools per worker is a performance optimization, not just a security measure.
Domain Knowledge
Documents ingested into a knowledge base and assigned to the worker. Relevant chunks are retrieved via semantic search and injected into context automatically. Update a document once and every worker that references it picks up the change.
Persistent Memory
Workers remember across executions. Memory cascades through three levels — worker, tenant, organization — so related workers benefit from shared experience without being polluted by irrelevant context.
Human-in-the-Loop, Not Human-out-of-the-Loop
The human approval step pauses any workflow for a decision — approve, reject, or custom actions. SLA tracking, escalation when deadlines are missed, delegation, and secure callback URLs for external stakeholders who never need to log in.
Built for the Long Run, Not the Prototype
The hard part was never building the automation. It was operating it for the next three years.
Any Model. No Lock-In.
Six LLM provider templates — Azure OpenAI, OpenAI, Anthropic, Google Gemini, Mistral, and local models via Ollama. Per-tenant configuration. Switch providers with a dropdown, not a rewrite.
Enterprise-Grade by Default
Multi-tenant architecture with complete data isolation. Five-level role-based access control. Secrets in Azure Key Vault — platform-level and per-tenant. Sandboxed Python execution. Webhook signature verification. Full execution traces. Per-step cost tracking.
These aren't features you enable. They're the platform.
Start with one pain point.
One workflow. One AI Worker. One manual process that's costing you time or errors.
How Doozer Works
A detailed look at the platform behind your AI workforce.
Three Reasoning Modes
ReAct
Reason-and-act loop. Pick a tool, execute, read the result, decide next. Best for straightforward tasks — Q&A, lookups, notifications.
Plan-and-Execute
Full plan upfront, parallel execution where possible, then synthesis. Best for multi-step research and data gathering across sources.
Deep Thinking
Plan, Execute, Evaluate, Synthesize. The worker reviews its own output and decides if more iterations are needed. Best for complex analysis and self-correction.
Visual Workflow Designer
Describe what you want — we'll join the dots. Or take full control when you need it.
Actions
AI
Control Flow
Memory
People
Documents
Design-time validation — Required field checking, cycle detection, dependency analysis. Errors surface in the designer, not in production.
Your workers can operate any web application
No API? No problem. The AI browser agent logs into portals, fills forms, extracts data, and navigates multi-step processes — operating applications the same way you do, by looking at the screen and acting on what it sees.
Human-in-the-Loop
Monitoring and Observability
Real-Time Execution
Live step-by-step progress via SignalR. Data dictionary viewer with delta highlights between steps. Pause, resume, stop, or step-forward controls.
Activity Dashboard
Four tabs — Overview, Workflows, Team, Insights. Time-range filtering. Execution counts, success rates, failure diagnostics, cost breakdowns.
Execution Traces
Full trace per run: inputs, outputs, duration, token usage, cost, deep thinking sub-events, browser screenshots. The audit trail.
Why a Platform, Not Just AI-Generated Code
"Reliability will become a major competitive advantage. As these tools move into production workflows, consistency matters more than raw capability."— Nate Patel
The Operational Gap
AI coding tools produce working scripts. You still need somewhere to run them, credential management, monitoring, access control, retry policies, and cost tracking. On Doozer, these are platform services that exist on day one for every workflow.
The Day 2 Problem
Week 2: an API changes and the automation breaks silently. Month 1: someone needs to edit a value buried in code. Month 2: a better model drops and every automation needs updating. Month 3: finance wants a cost report nobody can produce. On Doozer, each of these is a config change, not a coding project.
The Proliferation Problem
50 standalone automations = 50 codebases, 50 deployments, 50 monitoring surfaces, nothing shared. On Doozer, the marginal cost of the 50th automation equals the 1st.
The Iteration Gap
Doozer workflows are self-documenting. The visual canvas shows the flow. Execution traces show how data moved. Changing a prompt is editing a text field. Adding a branch is dragging a connection. Anyone with access can iterate — no terminal required.
Solve a Problem Once. Share It with Everyone.
Three listing types — worker bundles, tool packs, and workflow packs. Deep-copied on install, so no shared mutable state. Seller profiles, reviews, ratings, and Stripe Connect for paid listings.