Trellis Connect
Legacy Modernization / Cloud Native Development / Platform Engineering
Executive brief
Mobile Emulation and Secure Data Collection in Kubernetes
Insurance quote workflows require speed and security. Epsilon optimized Trellis’s container startup path, tuned Kubernetes scheduling and autoscaling, and implemented security guardrails to support sensitive data and burst traffic—enabling real-time quote experiences.
Cold start time
10 min → 45 sec
post-optimization
Burst throughput
120/min → 450/min
post-scaling
Spike-time error rate
3.8% → 0.9%
post-hardening
Reduced startup time dramatically for just-in-time compute
Improved throughput and reduced spike-time error rates
Implemented security guardrails for sensitive data flows
Engagement shape
Duration
6 wks
Phases
3
Deliverables
4
Cold start time
10 min → 45 sec
How it ran
Performance optimization + scaling + security hardening
Trellis Connect achieved real-time readiness by cutting cold starts from ~10 minutes to ~45 seconds on Kubernetes.
Starting position
What Trellis Connect was working against.
A core quote service had ~10-minute startup time, making just-in-time compute impossible and harming user experience, while security requirements remained high.
Constraints we worked inside
3 in playEvery one of these ruled an easier option out. They are the reason the approach looks the way it does.
Cold starts were dominated by blocking init sequences
Traffic patterns were bursty and time-sensitive
Sensitive customer data required strong security posture
What had to be true to finish
3 goalsAgreed up front, so the engagement could be called finished on evidence instead of opinion.
Reduce startup time to under 1 minute
Improve throughput and reliability under spikes
Harden security controls and operational guardrails
Industry context
Legacy Modernization
Cloud Native Development
Platform Engineering
Trellis Connect helps customers compare insurance quotes in real time, requiring rapid startup and secure handling of sensitive information.
Approach
How the work was sequenced.
Epsilon optimized startup behavior, tuned Kubernetes runtime controls, and implemented security and observability guardrails.
Delivery track
Segment width reflects the number of workstreams inside each phase — where the engagement actually spent its effort.
Startup path optimization
3 workstreams
Removed blocking initialization and deferred non-critical work
Implemented accurate readiness checks aligned to real readiness
Tuned image build strategy to reduce pull and init time
Kubernetes scaling and stability
3 workstreams
Adjusted resource requests/limits to reduce scheduling delays
Introduced autoscaling tuned to burst traffic
Implemented safe rollout strategies and rollback patterns
Security hardening
3 workstreams
Least-privilege IAM patterns and secret handling
Network segmentation and policy baseline
Audit-friendly logging and access patterns
What the team kept
Artefacts handed over at the end of the engagement — owned and operable by Trellis Connect without us.
Optimized container build and startup approach
Kubernetes deployment templates and scaling configuration
Security guardrails and access controls baseline
Operational runbooks and alerting patterns
Results
What actually moved.
The service became real-time capable, more reliable under load, and more secure by design.
Outcome ledger
Every figure we measured on this engagement, including the ones that are ranges rather than headlines.
Cold start time
Illustrative
10 min
45 sec
Burst throughput
Illustrative
120/min
450/min
Spike-time error rate
Illustrative
3.8%
0.9%
What changed day to day
The part of the result that never shows up in a dashboard, but is the reason the numbers held.
Enabled near real-time quoting experiences
Improved reliability during traffic spikes
Strengthened security posture for sensitive user data
Your turn
Run this play with your team.
Trellis Connect achieved real-time readiness by cutting cold starts from ~10 minutes to ~45 seconds on Kubernetes. If that shape looks familiar, the first conversation is a working session, not a pitch.
Kubernetes performance optimization
Security hardening
Container startup optimization
Autoscaling strategy
What Trellis Connect got
Cold start time
10 min → 45 sec
post-optimization
Burst throughput
120/min → 450/min
post-scaling
Spike-time error rate
3.8% → 0.9%
post-hardening