From discovery to managed support.

One team takes the workflow from a whiteboard to production — and keeps it running.

  1. Step 1: Workflow Discovery

    Fixed fee

    We map the current process, systems involved, data sources, AI opportunities, security constraints, required approvals, expected volume and success metrics.

    Workflow architecture + implementation plan

  2. Step 2: Workflow Implementation

    Project-based

    We build the workflow: trigger, integration, AI processing, validation, human approval where required, and the business system at the end.

    A working, tested workflow

  3. Step 3: Deployment

    Your environment

    Each workflow ships as an independent containerized service into your AWS, Azure, Google Cloud, Kubernetes, Docker, private cloud or on-premise environment.

    Running in your environment

  4. Step 4: Managed Support

    Monthly

    Monitoring, troubleshooting, workflow and connector updates, prompt updates, model migration, dependency updates, bug fixes and performance tuning.

    Kept running, month after month

Every workflow runs independently.

Each production workflow gets its own container, version, scaling policy, logs, secrets, resource allocation and deployment lifecycle — isolated at runtime, managed centrally.

Container · every workflow ships with

  • Workflow
  • Dockerfile
  • Configuration
  • Secrets
  • Logging
  • Health check
  • Deployment
  • Documentation

Where it runs

  • Your AWS environment
  • Your Azure environment
  • Your Google Cloud environment
  • Kubernetes
  • Docker infrastructure
  • Private cloud
  • On-premise infrastructure

Bring the problem and the decision that is stuck.

In one hour we look at the workflow, the systems around it, and whether AI is the right tool, and leave you with a clear next step.

Book a discovery call
Issued for
Discovery
Duration
1 hour
Bring
The process, the systems, the stuck decision
Leave with
A clear next step