How we solve the hard parts

Screens are easy. We crack the hard problems.

Anyone can build a form. The real work is what happens under load — scale, streaming, native performance, automation that holds up. Here are the kinds of problems we've solved, and exactly how we tackled them.

KubernetesScaled & autoscaled
iOS · Android · WebFrom shared code
Self-healing AIPipelines that verify
NativeWhen it counts
Scale · streaming · cross-platform

Making a live-learning platform scale to spikes

The hard parts of a live-class platform: traffic spikes, video, and every device at once
01
KubernetesAutoscalingKotlin Multiplatform Custom video playerDRMPayments
The hard problem

A live-class platform gets hammered in unpredictable spikes — thousands of students join a stream at 7pm sharp — while also needing piracy-safe video and a consistent experience on iOS, Android and web. The screens were never the challenge; scale, streaming and cross-platform parity were.

How we cracked it

We ran the backend on Kubernetes with horizontal autoscaling, so capacity follows the crowd and cost drops when it's quiet. For video, we built our own player in Kotlin Multiplatform — one implementation shared across iOS, Android and web — and engineered it to scale, with DRM and per-user watermarking so content stays protected. Shared code meant every feature shipped to all platforms at once instead of being rebuilt three times.

What that took
  • Kubernetes with horizontal autoscaling that absorbs live-class traffic spikes and right-sizes cost
  • A custom video player built once in Kotlin Multiplatform — one player for iOS, Android and web
  • That player engineered to scale, with DRM and watermarking for piracy-safe streaming
  • Shared-code architecture so a single feature reaches every platform together
  • Integrated payments with an automated settlement-and-payout pipeline
Autoscaledon Kubernetes
1 player3 platforms
Piracy-safeDRM streaming
AI & automation

An AI pipeline that checks its own work

Making generated output trustworthy — not a one-shot gamble
02
Autonomous agentsMicroservicesMessage queue SandboxingVisual diffing
The hard problem

AI can generate something impressive in one shot — but a single prompt gives you no guarantee it's actually correct, and it rarely matches the target precisely. The hard problem is making AI output trustworthy and repeatable at scale, not just a demo.

How we cracked it

We built a self-healing agent loop: generate the output, actually compile and run it in a sandbox, then verify it automatically with visual diffing against the target — iterating until it clears a strict similarity bar. We ran it as decoupled services over a message queue with horizontally scalable workers, so it stays fast and reliable under load instead of falling over on a big job.

What that took
  • A closed feedback loop: generate → build in a sandbox → verify → auto-iterate until it passes
  • Real compilation and execution of generated output before it's ever accepted
  • A decoupled microservice architecture over a message queue, with workers that scale horizontally
  • Automated visual verification so quality is measured, not assumed
Self-validating
Measurednot assumed
Scaleshorizontally
Web performance & accessibility

A premium site that still loads instantly

Editorial polish, zero-server hosting, bulletproof on mobile
03
Static-firstTiny JSReduced-motion AccessibilityDeploy anywhere
The hard problem

Clients want rich, animated, premium sites — but not at the cost of speed, hosting bills or accessibility. Beautiful and genuinely fast usually pull against each other.

How we cracked it

We build static-first: the whole site compiles to plain HTML/CSS/JS that any host serves with no server to run or scale. We keep JavaScript tiny, gate animation behind the visitor's reduced-motion setting, and run a rigorous accessibility and mobile pass — labelled inputs, visible focus, proper touch targets, safe-area insets. Premium feel, instant load, deploys anywhere.

What that took
  • A fully static build — no server to run, deployable to any host or CDN
  • Rich motion that respects reduced-motion and never blocks the main thread
  • An accessibility + mobile pass baked into the build, not bolted on
  • Tiny shared JavaScript and self-hosted fonts to kill layout shift
Staticdeploy anywhere
Instantfirst load
A11yby default
Native mobile & telephony

Going native where cross-platform stops

Calling, dialpad and messaging that behave like the OS built them
04
Native modulesTelephonyCall UIReusable APIs
The hard problem

Cross-platform frameworks get you 90% of the way — then calling, telephony and real device features need fragile native bridges that break on every OS update. That last 10% is where most teams get stuck.

How we cracked it

We build the native layer properly and package it as reusable modules behind a clean, cross-platform API — native call UI, a dialpad, a messaging layer — so comms features feel first-class on both platforms without rebuilding the bridge for every project.

What that took
  • Native call-UI integration that behaves like the system dialer
  • Reusable dialpad and messaging modules with a simple cross-platform API
  • Telephony features engineered to survive OS updates instead of breaking
Nativecall UI
Reusablemodules
Cross-platformcomms
E-commerce

Storefronts tuned to sell, not just display

Custom commerce shaped around how a merchant actually sells
05
Custom themesConversion-focusedPerformance
The hard problem

Off-the-shelf commerce themes look generic and convert like it — the store ends up looking like everyone else's and never quite fits how the business actually sells.

How we cracked it

We build custom storefronts shaped around a merchant's brand and catalogue — fast, conversion-focused, with sections designed around their real products rather than a template's assumptions.

What that took
  • Bespoke, brand-first storefront themes
  • Conversion-oriented layout and product presentation
  • A performance-minded front end that keeps the store fast
Customstorefront
Conversionfocused
Cross-platform & internal tooling

One codebase, every platform — plus the tools around it

Shared-code apps, and the dashboards and automation that run them
06
Kotlin MultiplatformFlutterDashboardsAutomation
The hard problem

Shipping to iOS and Android usually means two codebases, two teams and features that quietly drift apart — and behind every product is a pile of manual internal work that never scales.

How we cracked it

We share code across iOS and Android with Kotlin Multiplatform and Flutter so one feature pipeline lands on both stores — and we build the dashboards and automation tools that take the manual grind out of actually running the product.

What that took
  • Shared-code apps across iOS and Android with native-quality results
  • Internal dashboards and admin tooling to run operations
  • Workflow automation that removes repetitive manual steps
1shared codebase
iOS + Androidtogether
The point

Different domains, same instinct: find the hard part and solve it.

Kubernetes & autoscalingScalable video & DRM Kotlin MultiplatformAutonomous AI pipelines Microservices & queuesStatic, instant-load web Native mobile modulesPayments & payouts Dashboards & automation
Have a hard problem?

Bring us the part that scares you.

The bottleneck, the thing that won't scale, the integration nobody wants to touch. Tell us what it is — we'll come back with an honest plan.

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