Automate the work between your systems

The problem

Cron jobs and scripts don't scale into a business process

Most organizations run critical processes on a patchwork of scheduled scripts, manual hand-offs and tools that don't talk to each other. Failures surface late, often reported by the business, and the knowledge sits with a few people. Orchestration replaces that patchwork with workflows that are versioned, observable and recoverable.

What we automate

• Business process automation

  What it deliversApprovals, onboarding, claims, reconciliations and back-office flows, with human-in-the-loop steps where judgment matters

• Data pipelines and ELT

  What it deliversBatch and incremental pipelines from source systems to warehouse and lakehouse, with tests, lineage and freshness alerts

• Event-driven architecture

  What it deliversKafka, RabbitMQ and cloud queues that let systems react to business events as they happen

• AI workflow orchestration

  What it deliversRAG ingestion, embedding refresh, batch inference and agent workflows, with every model call logged, retryable and auditable

• Integration automation

  What it deliversSaaS, ERP and CRM integrations through APIs and connectors, replacing manual exports and re-keying

• Data engineering and analytics

  What it deliversApache Spark processing, lakehouse design and BI dashboards that turn pipelines into decisions

• Data quality and governance

  What it deliversSchema registries, data contracts, quality checks and lineage that keep downstream consumers confident

• Automation platform and CoE

  What it deliversStandards, templates and self-service tooling so teams can build automations safely on a shared platform

Choosing the right orchestrator

The right engine for each job, operated as one system

• Apache Airflow

  Best forData, ML and AI pipelines with complex dependencies; Python-native teams; asset- and event-driven scheduling

  Watch forNeeds disciplined DAG design and real operational ownership at scale

• AWS Step Functions

  Best forServerless service orchestration inside AWS; short-lived, high-volume state machines

  Watch forAWS-specific; less natural for data-heavy batch dependencies

• Azure Durable Functions

  Best forLong-running, stateful application workflows on Azure

  Watch forAzure-specific; code-centric, with less visibility for data teams

• Apache Kafka

  Best forHigh-throughput event streaming and decoupling between systems

  Watch forMoves events; it doesn't orchestrate multi-step business logic on its own

• AI agents

  Best forSteps that need judgment over unstructured inputs

  Watch forNon-deterministic; wrap them in orchestration with approvals, limits and audit trails

Most enterprises need more than one. We put each engine where it fits and connect them, so one team can see and operate the whole flow. See how we pair them in event-driven automation with Apache Airflow and Kafka.

Automation principles

1. 1

   Idempotent by design: every task can be re-run safely.

2. 2

   Failure is a first-class path: retries, dead-letter queues and alerts designed up front, not after the first incident.

3. 3

   Observable end to end: lineage, run history and service-level dashboards for every workflow.

4. 4

   People where judgment matters: approval gates with timeouts and escalation.

5. 5

   Automation as code: versioned, reviewed, tested and deployed through CI/CD.

AI meets automation

Bring AI into business processes with the controls auditors expect

Orchestration is becoming the safest place to run AI. Apache Airflow 3.1 added human-in-the-loop steps, and the community's Common AI provider (April 2026) turns LLM calls and agents into named, logged, retryable tasks with optional approval gates. We use these patterns to automate judgment-heavy work without losing the audit trail.

Read our perspective: The orchestration layer is becoming the control plane for enterprise AI

FAQs