eCommerce Performance & Reliability
Find and remove the storefront, API and operational bottlenecks that hurt conversion and make peak traffic risky.
A service shaped around the constraints that slow your business down.
- Core Web Vitals fail on real mobile traffic
- Checkout slows down under load
- Third-party scripts block critical journeys
- Incidents lack useful traces and ownership
- Field-data performance budget
- Faster critical customer journeys
- Capacity evidence before peak events
- Actionable monitoring and incident runbooks
What the engagement can include.
Core Web Vitals and frontend diagnostics
API and backend latency tracing
Database query and connection analysis
Cache behaviour and invalidation review
Queue throughput and delay analysis
Load, capacity and infrastructure testing
Measure the complete commerce path
A Lighthouse score is one signal; reliability depends on the full path from browser to infrastructure.
User experience
LCP, INP, CLS and TTFB describe real storefront responsiveness.
Application path
API latency and p95/p99 expose slow or inconsistent backend behaviour.
Commerce health
Checkout errors and queue delay connect technical issues to critical workflows.
Capacity
Load tests and production telemetry validate cache, database and infrastructure limits.
From uncertainty to production evidence.
Measure
Combine field data, traces, logs and controlled tests.
Prioritise
Rank bottlenecks by user and revenue impact.
Improve
Fix critical rendering, data and infrastructure paths.
Prove
Compare production measurements and define regression gates.
Concrete decisions, working assets and a clear next move.
Core Web Vitals and journey audit
Included in the engagement
Backend and dependency analysis
Included in the engagement
Prioritised remediation backlog
Included in the engagement
Performance budgets and monitoring plan
Included in the engagement
Checkout slows down during campaign traffic
Starting point
Field data shows unstable page experience while API latency and queue backlogs rise during demand peaks.
Engineering response
The team correlates LCP, INP, CLS and TTFB with traces, database activity, cache efficiency, workers and infrastructure capacity.
Expected business result
Bottlenecks become measurable, remediation is prioritised by journey impact and regression limits protect future releases.
Designed to stay understandable after launch.
What teams ask before starting.
Can you guarantee a Lighthouse score?
No. We optimise for representative users and business journeys; a lab score alone does not describe production experience.
Do you work on backend performance?
Yes. We trace storefront requests through APIs, databases, caches and external services to find the actual constraint.
How do you prepare for peak traffic?
We model demand, load-test critical paths, verify degradation behaviour and document capacity and recovery limits.
Which metrics do you track?
We select metrics by journey and usually include LCP, INP, CLS, TTFB, API latency, p95/p99, checkout errors and queue delay.
Can you investigate intermittent production slowdowns?
Yes. Distributed traces, structured logs, database evidence and infrastructure metrics help correlate short-lived degradation across the full request path.