We design automation systems around real-time events instead of static workflows, using technologies like Kafka or NATS to trigger, coordinate, and scale actions across APIs, services, and user-facing systems.

Our execution layers persist task state, track side effects, and support compensation logic, enabling robust recovery and idempotent flows under failure conditions.

We embed LLM-based decision points using structured prompt engineering, response validation, and fallbacks to deterministic logic where confidence thresholds are unmet.

We automate through your backend and internal APIs whenever possible, avoiding brittle UI scraping or low-code middleware limitations.

Our systems enable runtime agent selection, multi-agent collaboration, and shared memory architectures, allowing adaptive execution paths based on task metadata.

We expose per-task telemetry, trace chains of events and model interactions, and support full output provenance to meet audit and debugging standards.

Each automation context runs within a scoped identity and permission set, ensuring compliance with zero trust, data isolation, and internal security models.

We ship Helm charts, CI/CD pipelines, and Kubernetes-native configurations, optimized for cloud, hybrid, or on-prem deployment with secrets, RBAC, and monitoring pre-integrated.