We build the data systems that turn information into action — pipelines, orchestration, and AI infrastructure built for scale.
From raw ingestion to intelligent outputs, Pivital engineers every layer of the stack with precision and reliability.
We architect and implement high-throughput data pipelines that ingest, validate, and route information with fault-tolerant precision — built on Kafka, Flink, dbt, and custom orchestration layers.
Batch + StreamingEnd-to-end automation systems that eliminate manual intervention — from event-driven triggers and workflow orchestration to multi-system process automation running at enterprise scale.
Event-DrivenInfrastructure engineered for model deployment and inference at scale — feature stores, vector databases, model registries, and the data contracts that keep AI systems grounded in reality.
MLOps · LLMOpsFull-stack observability across your data systems — lineage tracking, anomaly detection, SLA monitoring, and incident response frameworks that keep operations transparent and resilient.
SLO · Lineage · AlertingA continuous intelligence loop — from raw data to automated action and AI-powered insight.
Multi-source collection across APIs, streams, databases, and edge systems with schema enforcement and lineage tracking from the first byte.
Declarative and code-first transformations — cleaning, enrichment, normalization, and feature engineering at any scale.
DAG-based workflows with dependency resolution, retry logic, dynamic branching, and cross-system coordination.
Event-driven actions that initiate business processes, API calls, notifications, and downstream system updates without human latency.
Real-time model inference integrated into the pipeline — scoring, classification, generation, and feedback loops that improve over time.
Technically deep, industry-agnostic solutions engineered for the complexities of modern data environments.
We design and implement automation frameworks that eliminate operational bottlenecks. From data-triggered workflows to multi-system process automation, every system we build is deterministic, auditable, and built to run without intervention.
Sub-second data processing pipelines designed for high-velocity environments. Stream processing, complex event detection, stateful computation, and low-latency serving layers that turn live data into live decisions.
We build the data foundations that make AI models trustworthy in production — feature stores, ground-truth pipelines, evaluation frameworks, and the operational infrastructure to deploy and monitor models at scale.
Cloud-native and hybrid data infrastructure designed for growth — multi-region replication, fine-grained access control, compliance-ready data governance, and infrastructure-as-code patterns that scale cleanly.
Pivital was founded on the principle that AI is only as reliable as the data systems beneath it. We exist to build that foundation — with engineering rigor, operational discipline, and a bias toward long-term system health over short-term velocity.
Tell us what you're building. We'll tell you how to engineer it right.