End-to-end digital assurance, site reliability and application optimization — engineered so your platforms stay fast and available as traffic grows.
Fit finder
Tap an option — the answer appears instantly.
You picked
A launch, sale, results day or campaign is coming and the platform has to hold.
What we run
What you get
Service pillars
From pre-release load tests to production reliability and code-level tuning — one team owns performance end to end.
Automated, multi-protocol simulation gates before code reaches production.
Performance & Load Testing
Load, peak traffic, stress, capacity, spike and event-readiness testing.
Digital Experience Assurance
Omnichannel, browser, API and microservices benchmarking.
Continuous Quality Integration
Performance gates in CI/CD pipelines and shift-left performance consulting (PTDD).
Infrastructure resilience and continuous observability, built for availability.
System Resilience Engineering
Endurance and soak testing, chaos engineering and scalability analysis.
Monitoring & Observability
APM setup, real user monitoring and synthetic alerting.
Production Incident Engineering
Post-incident performance forensics and root-cause analysis.
Deep-dive diagnostics and code-level tuning to maximize speed and minimize overhead.
Performance Optimization & Consulting
Architectural bottleneck analysis and database tuning.
Cloud & Infrastructure Engineering
FinOps cloud cost optimization, CDN and latency reduction.
Overview
We pick the test types your risk actually needs — not a fixed menu.
Does the system meet its targets at expected traffic?
Where does it break, and does it fail gracefully?
What happens when traffic jumps in seconds, not hours?
Do memory leaks, connection exhaustion or slow drift appear over hours?
How much headroom is there, and does adding resources actually help?
Which service, endpoint or query is the weakest link in the chain?
Outcomes
Performance work that pays for itself — in fewer incidents, faster pages and smaller cloud bills.
Catch degradation early in the pipeline, before it turns into a costly rewrite.
Native experience with k6, Gatling, JMeter, LoadRunner and NeoLoad — we work with your stack, not ours.
We tune code and infrastructure to cut cloud spend without giving up performance.
Metrics & deliverables
Every engagement ends with numbers you can act on and assets you keep.
Sample report
An illustrative example of the summary page every engagement ends with.
Peak virtual users
5,000
Target 5,000 · Achieved
Throughput
1,340 req/s
Target 1,200 req/s
p95 response time
640 ms
Target < 800 ms
p99 response time
!1,710 ms
Target < 1,500 ms
Error rate
0.4%
Target < 1%
DB connection pool at peak
!92%utilised
CPU at peak (app tier)
68%
Cost per 1,000 requests
₹0.42
Baseline ₹0.61 · −31% after tuning
Response time under load
Top findings
Checkout DB connection pool saturates at ~4,600 users; p99 spikes follow.
Raise pool size and add a read replica for order lookups.
Product-image requests bypass the CDN on cache miss.
Fix cache headers; expected origin load −40%.
Unused autoscaling headroom overnight.
Schedule scale-down; lowers baseline cost.
Next step: re-test after fixes on 2 build candidates
Report includes: full graphs per scenario, raw k6 results, APM traces, capacity model.
Process
Targets first, then evidence, then fixes — and monitoring so the gains hold.
Business-critical journeys, KPIs and SLAs mapped, with the traffic peaks you actually need to survive.
High-fidelity traffic simulations with k6 or Gatling against realistic data and user behaviour.
APM traces and logs, database tuning and code refinement aimed at the real bottleneck.
Round-the-clock observability, alerting and self-healing setups so performance holds as you grow.
Tech stack
Chosen per engagement — we work with the tools you already run.
Engagement model
Start with a single test cycle or make performance a continuous practice.
A fixed-scope cycle: workload model, test runs, fixes and a go/no-go before your date.
Best for: Launches, sale days, results days and campaigns.
Diagnose the bottlenecks, implement or guide the fixes, and prove the gain with a re-test.
Best for: Slow applications, rising cloud bills and recurring incidents.
Pipeline gates, scheduled regression runs, observability and SRE support on a monthly retainer.
Best for: Teams releasing weekly or daily.
Industries served
Sale days, results days, month-end close, open enrolment — the same discipline, applied to the moment your platform can't afford to slow down.
FAQ
Share your architecture and your next traffic peak. We'll review the infrastructure and show you where it bends first.
