Automated testing,
under realistic conditions

We run QA against your app on dedicated infrastructure. You set the volume, view time and behaviour — everything else runs itself.

See plans How the system works

Parallel runs

capacity scales with your server

Realistic journeys

multi-page, natural pacing

Your parameters

target, volume, view time, clicks

Core capability

Higher QA quality. Realistic runs. Full control.

This is the whole discipline we specialise in — nothing bolted on. Automated QA that produces signal your team can actually act on.

Realistic visit behaviour

View times, scroll depth, click paths and idle gaps follow the rules you define, so results reflect how real users move through your app.

Behaviour-driven interaction

Scroll curves, hover timing, click paths, form input pacing and idle gaps — all defined by you as reusable behaviour rules.

Flexible proxy supply

Bring your own proxy links or attach a managed pool at checkout. We handle sourcing and delivery; you manage bandwidth.

Scalable capacity

Run density is tuned to your machine: cores, RAM and bandwidth map directly to how many parallel QA runs you can drive.

Stronger view quality

Runs hold realistic time-on-page, multi-page journeys and return patterns instead of shallow single-page checks.

Set your own parameters

Target, volume, view time, pages per run, clicks and schedule — all configured in your dashboard and applied to your node within seconds.

Ultra smart system

One loop: define, distribute, adapt, report.

The engine is rule-driven, not script-driven. You express intent; the system handles pacing and recovery across every server you run.

Define

Write test rules

Describe how a run should act — entry source, pages to visit, view-time ranges, scroll depth, click targets, conversion attempts.

Distribute

Spread across your fleet

The scheduler splits the workload over your deployed servers, respecting concurrency and rate ceilings.

Adapt

Automatic recovery

Failed or stalled runs are retried and pacing is adjusted automatically so quality stays steady across the day.

Report

Review

Each cycle closes with pass/fail signals and a run summary you can check in your dashboard.

Dedicated setup

Pick your server type. Your hardware sets your ceiling.

We deploy onto the machine class you choose. The stronger the server, the more parallel QA runs it sustains — bandwidth and RAM are the real limits.

Starter node

4 vCPU · 8 GB RAM · 1 Gbps

Best for prototyping runs and single-flow QA scripts.

Light capacity

Performance node

8–16 vCPU · 32 GB RAM · 1–2 Gbps

Balanced choice for continuous multi-journey QA cycles.

Medium capacity

Dedicated bare metal

32+ vCPU · 64–128 GB RAM · 10 Gbps

Maximum parallel runs with full hardware isolation.

Heavy capacity

Distributed cluster

Multi-region nodes · orchestrated

Runs spread across several machines under one dashboard.

Scaled capacity

Every deployment includes

Server hosting itself is billed separately from setup — you keep ownership of the machine and the proxy supply.

Dedicated server, provisioned and hardened for you
Run orchestrator + scheduler service
Proxy connector layer (your links or our managed pool)
Live metrics agent piped to your dashboard

Control dashboard

Manage your QA work exactly how you want it.

Add behaviour rules, assign them to nodes, and watch run health in real time. No tickets, no waiting on us.

Active runs

551

across 4 nodes

Runs today

12.4k

last 24h

Avg. view time

2m 14s

per run

Run throughput

live

Node activity

Run	Node	Parallel	View time	State
SQ-9412	Bare metal	184	1m 52s	online
SQ-9413	Performance	96	2m 07s	online
SQ-9414	Starter	31	0m 48s	Retrying
SQ-9415	Cluster	240	3m 14s	online

Behaviour rules

Runs per day 10kView time 45–180sPages per run 1–3Random clicks onMouse movementReturning visitors 15%Schedule 07–23 dailyEven pacing

Night skyline representing a commerce platform

Engagement snapshot

A commerce platform validated its checkout under sustained load

Behaviour rules drove multi-page journeys into checkout on a dedicated bare-metal node. Sustained load surfaced two race conditions that only appeared above 150 parallel runs — exactly the kind of finding QA exists to catch.

6×

more parallel runs after node upgrade

2m 14s

average view time held across runs

race conditions surfaced under sustained load

FAQ

Before you apply

Anything not covered here gets answered in the scoping call.

Automated testing,under realistic conditions