BigPanda

From tribal knowledge to scaled software maturity

Case Study Summary

When BigPanda’s platform‑engineering group hit scale, the tribal‑knowledge approach to service ownership and standards began to drag. After adopting OpsLevel, they turned governance into a self‑service workflow that developers actually use.

4 engineering days saved every time a cross‑team standards or remediation campaign runs.
30‑minute reduction in MTTR for high‑severity incidents thanks to alert enrichment.
OpsLevel Actions shrank pull‑request checklist time from 1.5 hours to 12 seconds.
100% increase in services passing the GiantPanda Ready rubric within six months.
50% of all service owners onboarded in the first 90 days—without extra meetings.

Engineering footprint: 250+ components, 100+ users.

About BigPanda

BigPanda helps the world’s largest enterprises tame alert storms and resolve incidents faster through AI‑driven correlation and automation. Internally, the engineering team applies the same rigor to its own platform and turned to OpsLevel to accelerate that journey.

Challenge: visibility, velocity, and trust

Fragmented ownership. “Before we had the maturity rubric, it was difficult to know who’s GiantPanda‑ready and who’s not.” — Aaron Johnson, Senior SRE
Manual, error‑prone standards roll‑outs. A new Node.js LTS or playbook requirement meant chasing dozens of teams individually.‍
Risky blind spots. A deprecated Node module slipped into production, surfacing only after customer‑impacting incidents.

Solution: a catalog‑first approach with OpsLevel

OpsLevel is easier to populate than ServiceNow has ever been. —Aaron Johnson, Senior SRE

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OpsLevel capability	How BigPanda uses it	Business impact
Service Detection & YAML Ownership	Parses repo metadata to auto-register services and assign owners.	Catalog freshness without human data entry.
Maturity Rubrics (Scorecards)	“Giant Panda Ready” rubric gates onboarding; levels track progress.	100% increase in compliance in 6 months.
Package Manager + Checks	Scan manifests for vulnerable or disallowed modules.	Identified & remediated risky Node module in hours.
Actions	One-click workflow spins up the correct CI template & PR checklist.	PR effort dropped 1.5 hours → 12 seconds.
Alert-Data Export	Feeds enriched service context into BigPanda’s incident pipeline.	30 minutes faster MTTR for Sev-1s.

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Implementation timeline

Kick‑off to first value in under four weeks.

Week 1: Connect and crawl. GitHub, PagerDuty, and Terraform connected; 250+ services detected automatically.

Week 2: Define standards. Engineering leadership codified the GiantPanda Ready rubric (ownership, runtime, playbook link).

Week 3: Quick wins. Package Manager flagged the deprecated Node module; teams used an OpsLevel Action to bulk‑fix affected repos.

Week 4: Scale adoption. Self‑service onboarding guide plus Slack reminders drove 50% owner adoption in 90 days.

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Metric	Before OpsLevel	After OpsLevel	Δ (Delta)
Standards Campaign Effort	≈ 5 engineering days	≤ 1 day	4 days saved
PR Checklist Time	1.5 hours	12 seconds	≈ 450× faster
“Giant Panda Ready” Compliance	45%	90%	2× increase

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Looking ahead

BigPanda is rolling out a new initiative that leverages Campaigns and Actions to automate enforcement: any service that drifts from GiantPanda Ready triggers an Action to guide the fix, before incidents impact customers.

Key takeaways

Visibility fuels velocity. A living catalog removes guesswork and unlocks self‑service governance.
Standards without the slog. OpsLevel’s Rubrics and Actions cut weeks of manual coordination to a few clicks.
Better data, faster fixes. Rich context inside alerts shaves critical minutes off incident response, protecting customer trust.

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