Pipeline maintenance down 80%, with auto-recovery in minutes

A unified, AI-ready data platform for a global engineering software enterprise, bringing enterprise applications, cloud storage, APIs, and containerized environments together on resilient pipelines that take in 250GB+ a week.

Outcome

Pipeline maintenance down from 20 hours a week to 4, recovery in minutes where failures once meant manual 3am restarts, 80% of engineering effort redirected to new AI and ML features, and 100% of business reports on time.

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At a glance

Client & industry

Global engineering software enterprise, with operational data across observability, CMDB, identity, and ticketing systems

Engagement

Build and operation of a unified data platform, with warehousing, analytics reporting, and business communications owned by Agivant

Scale

250GB+ of new data a week from enterprise applications, cloud storage, APIs, and Kubernetes environments

Maps to below services

Data Engineering & Advanced Analytics

Where it started

Pipelines that failed at 3am and waited for a person, and silent failures that produced wrong reports.

Business problem

When a pipeline failed, someone had to restart it by hand, and recovery took around three hours. Maintenance cost grew linearly with data, so more data meant more staff.

Technical barriers

Operational data sat fragmented across observability, CMDB, identity, and ticket systems. 80% of engineering effort went into keeping the lights on, with 20 hours a week spent on pipeline maintenance.

Agivant's role

Data platform engineering, multi-source ingestion, self-healing pipelines, and data quality across the consolidated warehouse

What Agivant built

Agivant consolidated observability, CMDB, identity, and ticket data into one governed Snowflake warehouse, then built the agent layer on top of it. The platform takes in data from enterprise applications, cloud storage, APIs, Kubernetes, and operational systems through efficient, low-latency pipelines, processing 250GB+ of new data every week. A self-healing framework recovers failures automatically within minutes, anomaly detection stops bad data at the source, and workflows are orchestrated and monitored automatically. Value in days and weeks.

Unified operational warehouse

Observability, CMDB, identity, and ticket history in one governed Snowflake platform, centralized for enterprise-wide use.

Multi-source ingestion

Pipelines take in data from enterprise applications, cloud storage, APIs, Kubernetes, and operational systems.

Self-healing pipelines

Failures recover automatically within minutes, leaving engineers only the edge cases.

Orchestration and monitoring

Workflows are orchestrated and monitored automatically, with operational visibility across disparate systems.

Anomaly detection at the source

Enterprise-grade data quality checks stop bad data before it reaches a report.

Scalable architecture

Supports growing data volumes and new sources, with more data running on the same staff.

Reporting and business communications

Warehousing, analytics reporting, and business communications run by Agivant, with every report reaching the business on time.

Grounding layer for AI

Trusted, analytics-ready datasets power dashboards, analytics, and the agents built on the platform, including the 24/7 IT front door.

How it is built

Warehouse	Data domains	Ingestion sources	Pipeline recovery	Data quality	Consumers
Snowflake	Observability, CMDB, identity, ticket history	Enterprise applications, cloud storage, APIs, Kubernetes, operational systems	Self-healing framework with automated failure recovery	Anomaly detection and enterprise-grade quality checks	Dashboards, analytics, and AI agents

Before

Manual restarts after 3am failures, silent failures producing wrong reports, 80% of effort keeping the lights on, and cost that grew with data volume.

After

Auto-recovery within minutes, anomaly detection stopping bad data, 80% of effort on new AI and ML features, and more data on the same staff.

Proof in production

A pipeline fails at 3am and the self-healing framework recovers it within minutes, so data stays available to executives. Anomaly detection stops bad data at the source, which gives the business every report on time and more confidence in the GenAI outputs built on the same data. Pipeline upkeep dropped from 20 engineering hours a week to 4, edge cases only, and 80% of engineering effort now goes into new AI and ML features.

Measured results

Faster access to trusted data across teams, reduced downtime, and a cost curve that no longer tracks data volume.

80% down

Pipeline maintenance, 20 hrs a week to 4

Minutes

Recovery time, previously about 3 hours

80%

Engineering effort redirected to new AI and ML features

100%

Business reports on time

250GB+

New data ingested every week

Zero

Cost overruns

The repeatable pattern

Reusable asset

The unified warehouse doubles as the grounding layer, so agent output rests on defensible data, and the self-healing framework keeps recovery automatic as data volumes and sources grow. Every build amplifies the next.

Challenges overcome

Consolidating observability, CMDB, identity, and ticket history into one governed model, catching silent failures before they reached a report, and scaling data volume without adding staff.

Enterprise movement

Foundational

Operational

Path to autonomous