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Digital Resilience
Systems that can scale, be trusted and be accessible
Performance, security, accessibility and reliability engineering that ensures your platforms stay fast, secure and usable under real users, real load and real failures. AI agents hunt for weak points, and our engineers agree with you what has to hold.
from idea to impact, across the systems lifecycle, this service sits here
The challenge
Customers find most outages and breaches first
A minute-by-minute view of an outage
Select a moment to see the test that would have caught it earlier.
A routine release goes out with new retry logic for payments.
A load test at three times peak in the release pipeline, which would have exposed the retry storm before it shipped.
The payment provider slows down, and the new retries multiply traffic to it.
A rehearsed dependency failure, where the provider is slowed on purpose and confirmed whether checkout degrades gracefully.
Checkout response time passes the 3 second threshold, but no alert is sent.
An alert on the latency budget, plus synthetic checkout journeys that run every minute.
Customers notice first. Complaints start on social media and in the contact center.
Observability that ties infrastructure signals to the customer journey, so the team hears before customers do.
A manual rollback fails on a database schema change.
Automated rollback, rehearsed in a recovery drill, with schema changes that can run backwards.
Service is restored after 40 minutes of failed and abandoned checkouts.
A post-incident review that turns each gap above into a test that now runs on every release.
At a mean of $1.85M an hour for high-impact outages, 40 minutes costs about $1.2M.
New Relic Observability Forecast, 2026We test resilience the way production behaves – load models built from real traffic, security checks in every pipeline, controlled failure experiments and recovery drills – enabling you to know how your systems degrade, and how fast they recover.
Take the Digital Trust CheckWhat we do
We prove your systems hold up when it matters most
Across the SDLC, security and performance shift left and reliability shifts right into production.
AI-accelerated
Performance, stress and load engineering
Breaking points and the bottlenecks tested rigorously before go-live.
Production-like load models
Traffic patterns built from your analytics and logs, not guesses, across web, mobile and APIs.
AI-acceleratedStress, spike and soak tests
Peak events, sudden spikes and long runs that expose memory leaks and slow degradation.
Performance budgets in CI/CD
Response-time and throughput thresholds checked on every build, so regressions never reach users.
Capacity and cost tuning
Right-sized infrastructure that balances performance, resilience and cloud cost.
Cyber Security and penetration testing
Security checked on every build, and tested hands-on where it matters most.
Shift-left security automation
SAST, DAST and dependency scanning in the pipeline, with hard stops on critical vulnerabilities.
Penetration testing
Hands-on testing of applications, APIs and cloud, with fixes ranked by real exposure.
Secure software supply chain
Checks on third-party packages, build integrity and secrets before anything ships.
Configuration and identity reviews
SaaS and cloud settings, access rules and MFA tested, the gaps attackers look for first.
Accessibility, Usability, User Experience testing
Every experience made intuitive, accessible and reliable.
Accessibility audits
WCAG conformance testing with assistive technologies, plus automated checks in the pipeline.
Usability testing
Task-based sessions that show where real users hesitate, fail or drop off.
Cross-device and browser coverage
Consistent behavior across the devices, browsers and screen sizes that customers use.
Chaos Engineering
Controlled experiments that show how system degrades, before an outage happens.
Failure experiments
Planned faults in services, networks and infrastructure, run as game days with your teams.
Dependency failure testing
What happens when a third-party API, database or region goes down.
Blast-radius control
Experiments that start small, with automatic abort and rollback, so learning never becomes an incident.
Reliability and resilience testing
Proof that the platform meets its availability and recovery targets.
Failover and disaster recovery drills
Recovery time and recovery point objectives tested, not assumed.
Observability checks
Monitoring, alerts and traces verified, so failures are detected in minutes, not hours.
Automated rollback
Failed deployments return to the last working version automatically.
Self-healing operations
Known failure patterns detected and fixed automatically, with engineers alerted for the rest.
AI-acceleratedHow we deliver
We engineer failure scenarios and build resilience into everyday operations
Each engagement starts with a resilience exercise. What we uncover becomes automated validation for every release.
Baseline
Measure how the system behaves today, using real traffic patterns and the incident history. Agents turn the logs into load and attack models.
AI-assistedBreak
Run load, failure and attack scenarios inside an agreed blast radius, with a stop switch at every step.
Fix
Our engineers work through what failed with the teams, starting with what the end customers would feel first.
Prove
We re-run the scenario after each fix and file the result as release evidence. Then the run moves into your pipeline.
The cycle repeats before every major release or peak
Our delivery gating criteria
Every release candidate passes five gates.
- Release candidate
- LoadPasses when
Response time and error rate at two to three times expected peak
- FailurePasses when
Time to detect, how gracefully service degrades and time to recover
- SecurityPasses when
Critical and high findings closed before release
- RecoveryPasses when
Recovery time and data loss against the targets you have committed to
- AccessibilityPasses when
WCAG 2.2 AA conformance on the journeys that matter
- Release
How every gate is set
- Test against real trafficLoad and failure scenarios come from production telemetry and incident history, not guesses.
- Agree thresholds before testingLatency, error budgets and recovery-time targets are set with your business owners before any run.
- Control the blast radiusEvery chaos or security experiment has a scope, a stop switch and a rollback plan agreed in advance.
- Fix, then prove itFindings are re-tested after remediation, and the result goes into the release evidence.
Delivery accelerators
Move faster with confidence
Runbooks, scanners and pipeline templates readily available for baseline from day one.
- In buildAI-powered
Load model builder
Turns production logs and analytics into realistic load profiles for k6, JMeter or Gatling.
- In build
Chaos game-day kit
Experiment catalog, runbooks and scoring sheets for safe failure drills on cloud and Kubernetes.
- In build
Accessibility scan pack
Automated WCAG 2.2 checks in CI, plus a guided manual review checklist for key journeys.
- In build
Pipeline security gates
Ready-made SAST, dependency, secrets and container checks for common CI/CD platforms.
Try it
Downtime cost estimator
Put a number on an hour of downtime, and on faster recovery.
- Cost of one hour down
- $18K
- Yearly downtime cost
- $222K
- Indicative yearly saving
- $89K
Indicative only. Excludes SLA credits, penalties and reputational impact, which often cost more.
Get a tailored estimateInsights
From our security and reliability engineers
Find your breaking point before your customers do
Talk to our resilience engineers about where your platforms are most exposed, and how to prove they hold.