We build and operate production AI workflows for businesses.

Connect your models, documents, APIs, databases, and business systems into AI-powered workflows that run in your cloud, your infrastructure, or ours.

Book a discovery call How we work
Container · deployed in your environmentConfidence below thresholdInvoiceinboxV-100 · Your systemAI101OCRAI102ExtractionAI103ConfidenceRValidationR-201Human approvalHV-301AccountingsystemV-400 · Your systemContainer · your environmentLow confidenceInvoiceinboxV-100 · Your systemAI101OCRAI102ExtractionAI103ConfidenceRValidationR-201ApprovalHV-301AccountingsystemV-400 · Your system
  • EquipmentYour system
  • InstrumentAI step
  • LogicBusiness rule
  • Hand valveHuman approval
DrawingInvoice automationStatusExample, adapted per clientScaleNTSSheet1 of 1

A request we hear, drawn as a process.

When a customer uploads a document, extract the relevant information, validate it, send the result to our CRM, and alert someone when confidence is low.

You don’t need “Apache Camel development.” You need that outcome. We build the workflow behind it — connecting AI models, APIs, databases, files, email, messaging, CRMs, cloud services, document stores, event streams and internal systems.

Built for workflows where AI is part of the business logic.

Traditional automation connects applications. We build the workflows where a model has to classify, extract, judge its own confidence and hand off to a person.

Traditional automation

TriggerEventAPICallAPICallTriggerAPIAPI

AI workflow

Low confidenceTriggerV-100AI101ClassificationAI102ExtractionAI103ConfidenceRBusiness rulesR-201Human approvalHV-301EnterprisesystemV-400Low confidenceTriggerAI101ClassificationAI102ExtractionAI103ConfidenceRBusiness rulesR-201ApprovalHV-301EnterprisesystemV-400

Four steps from whiteboard to production. We stay for the fourth.

  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

    Engagement terms

  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

    See example workflows

  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

    Where it runs

  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

    Support plans

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