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Cloud Infrastructure · live demo

Survive any traffic spike without rewriting your stack

Black Friday, a viral article, a marketing push: traffic arrives all at once. Legacy hosting collapses, cloud-native infrastructure scales itself out, keeps response under 100 ms, and scales back down once the burst is over. Same traffic spike on a fixed VPS versus an autoscaled cloud, side by side.

05xx errors at peak
8×capacity on demand
<100msresponse under load
Live demo

How it actually works, step by step

No engineering jargon, no system migration as a prerequisite. This is exactly what the client gets the day we go live.

  1. 01

    1) Forecasts the spike

    AI combines historical curves, calendar of campaigns, ad spend and queue depth. Forecasts load ahead of time, not after seeing it in latencies.

    Black Friday Ad spend ↑ 4× Spike in 38 min
    Forecast 9,200 req/s at 8 PM, P95 latency target < 100 msAI
  2. 02

    2) Scales out before the spike hits

    Spins up additional instances / pods before the spike. Stateless services come up, stateful ones have reserved capacity. No "cold start" at peak.

    Pods · web ready
    Before4 pods
    After scale-out32 pods
    Warm-up42 s
  3. 03

    3) Spreads the load

    Load balancer + edge cache routes traffic across regions, spares the origin. Health gates cut bad instances out without waiting.

    EU-CENTRALEU-WEST Cloudflare edge
    63% of traffic served from edge cache, origin at 38% loadLB
  4. 04

    4) Holds response under 100 ms

    P95 latency stable even at 10× load. No 503s, no "temporarily unavailable", the user never notices.

    Latency · last 30 min SLO OK
    P5038 ms
    P9592 ms
    P99148 ms
    5xx0.00 %
  5. 05

    5) Scale-down + cost report

    After the peak, instances drop, infra returns to normal capacity. CFO sees the precise cost of the spike in the morning, SRE gets per-service attribution.

    FinOps report · Black Friday
    Peak cost $284, 9,200 req/s, 0 outages. Scale-down at 10:14 PM, back to baseline.
    Duration
    2 h 14 min
    Orders
    4 218
Who it fits

Tailored to your type of operation

E-commerce + D2C

Survives Black Friday, Christmas, sales campaigns. Auto-scales on the predicted curve, not a panic-react curve. Costs predictable in advance.

Media + publishing

Viral article, video drop, breaking news spike. Edge caching + autoscaled origin so the first wave does not collapse the site.

SaaS scale-up

From hundreds to millions of users. Multi-region failover, blue-green deploys, FinOps reporting so growth does not silently destroy your margin.

Public sector + elections

Predictable traffic surges (election night, results pages, application deadlines). Infra that holds without manual war room.

iGaming + live events

Match-day spikes, payout windows, leaderboard refreshes. Sub-100ms response under 10× normal load, regional failover ready.

API providers + fintech

Latency-sensitive APIs with bursty client traffic. Auto-scaled compute + queue absorbers, P95 latency stays predictable.

What the AI handles

Features you get from day one

Predictive autoscaling

Forecasts load from historical curves + live signal (ad spend, campaign clicks, queue depth). Scales out before the spike, not during.

Multi-region failover

Active-active in 2+ regions, automatic failover on health check loss. RPO seconds, RTO under a minute for stateless services.

Cost-optimised right-sizing

Nightly analysis of actual usage vs. provisioned. Recommends instance class changes, spot vs. on-demand mix, reserved capacity opportunities.

Container orchestration

Kubernetes managed with sensible defaults (HPA, PDB, anti-affinity). No 8-month learning curve before the platform is reliable.

Blue-green + canary deploys

Zero-downtime releases with automatic rollback on error-rate spike. Canary 5% → 25% → 100% with traffic-shifted health gates.

Built-in observability

Logs, metrics, traces wired from day one. No need to bolt Grafana + Loki + Tempo yourself, no missing correlation IDs.

FinOps reporting

Per-service, per-environment cost. Trend, forecast, anomaly. CFO gets a one-pager, the SRE sees attribution down to a workload.

SLO + error-budget alerting

Defined SLOs per service, error-budget burn alerts in Slack. Predictable on-call instead of "alert fatigue".

Customer perspective

Step by step on the user side

  • Without503 errors, lost customers, manual scramble at 2 AM
  • Withload detected, instances spin up, response stays low, no one notices
Owner perspective · head of engineering

Head of Engineering

Predictable cost (pay only for capacity actually used)
zero on-call incidents
infra ready for any growth.
Integrations

Plugs into your existing stack

PMS platforms, channel managers, smart locks and messaging channels. If your system isn't listed, just ask, most integrations ship in a few days.

  • AWS
  • Microsoft Azure
  • Google Cloud Platform
  • Kubernetes (EKS / AKS / GKE)
  • Cloudflare
  • Vercel
  • Fastly
  • Terraform / Pulumi
  • Datadog / Grafana
  • Custom observability stack
Why it makes sense

What this solution gives your business

Live in weeks
No months of prep work. We launch a pilot in weeks and roll out the full deployment right after it validates.
Your data stays with you
Runs on your infrastructure or an EU region. Encryption, audit logs, GDPR and NIS2 compliance are the baseline.
Custom integrations
CRM, ERP, helpdesk, e-shop, phone system, we connect to your existing stack instead of rewriting it.
Long-term ownership
Monitoring, model tuning, regular reports. We don't just hand over the project, we own the outcome with you.
Ready for a demo of your own?

Your Idea? We'll Build It.

Every business is unique. Tell us what takes your team the most time, and we'll design an AI agent tailored exactly to your needs. From concept to deployment in weeks, not months.

Let's Discuss Your Idea