Service

AI Automation

Input · Process · Decision · Output

We design automation systems where AI carries the judgment: document flows, approvals, operations, and service processes that run themselves and escalate only when they should.

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The problem

Manual processes do not scale judgment

Most automation stops at moving data between systems. The expensive part of a process is the decision inside it: what a document means, whether a case is an exception, which action comes next. Until that judgment is engineered into the flow, people remain the bottleneck.

What we do

Capabilities

01

Intelligent document processing

Extraction, classification, and validation of documents with language models and structured review loops.

02

Decision automation

Rules and models combined into auditable decision points with confidence thresholds and human escalation.

03

Process orchestration

End-to-end workflow engines that coordinate systems, people, and AI steps with full traceability.

04

Agentic task execution

AI agents that carry multi-step tasks across tools under explicit permissions and review gates.

How it works

The path from idea to operation

  1. 01

    Map

    Trace the real process, its exceptions, and where judgment actually lives.

  2. 02

    Model

    Select the decisions AI can carry and define confidence and escalation rules.

  3. 03

    Orchestrate

    Build the workflow engine that connects systems, models, and reviewers.

  4. 04

    Prove

    Run supervised in parallel with the human process until accuracy is demonstrated.

  5. 05

    Operate

    Deploy with monitoring, audit trails, and continuous evaluation.

Use cases

  • Invoice and document intake
  • Case triage and routing
  • Compliance checks and reporting
  • Customer service resolution flows
  • Back-office reconciliation

Technology

AI

Claude · OpenAI · open-weight LLMs · OCR pipelines

Orchestration

Temporal · Airflow · queue-based workers

Integration

REST · GraphQL · webhooks · RPA bridges

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