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Scale the system, tune the runtime before it breaks under load.

When the product outgrows the architecture, we re-platform without re-writing. Pure performance work or full architecture redesign, sized to the actual bottleneck.

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Service
Engineering
2 wk
To identified bottlenecks
ADRs
Architecture decisions on record
Measured
Gains in LCP, INP, throughput
Your team
Owns the fixes after handoff
Overview

Why this engagement exists.

Performance regressions and scaling cliffs are almost always cheaper to fix early. We start by measuring the actual bottlenecks, not the assumed ones, then implement high-impact fixes and leave behind a set of architecture decision records your team owns.

What you get

Deliverables, not promises.

Every engagement ships these artefacts. Nothing here is fluff. Each item is something your team will hold in their hands at the end.

Performance audit

Benchmark report against your real workload, not synthetic Lighthouse runs.

Architecture diagnostic

Current-state architecture diagram with annotated risk + recommended ADRs.

High-impact fixes

We implement the top 3-5 fixes that move the metric, not the long-tail polish.

Load testing + capacity

Realistic load tests + a capacity model showing when the next bottleneck hits.

Performance budgets

Per-route budgets wired into CI so the regressions get caught before merge.

Runbook + handoff

Knowledge transfer sessions + a runbook so your team owns the optimisation pattern.

How we work

The process, step by step.

No mystery, no consultant theatre. This is how the work actually flows from kickoff to handover.

  1. Step 1

    Profile + benchmark

    Real-traffic profiling, not estimates. We find the actual bottlenecks, not the imagined ones.

  2. Step 2

    ADRs + recommendations

    Document the decisions. Trade-offs explicit. Stakeholder sign-off before code is touched.

  3. Step 3

    High-impact first

    Implement the 3-5 fixes that move the metric. Polish later; ship the wins first.

  4. Step 4

    Load test + capacity

    Validate the fixes hold under realistic load. Capacity model shows the next ceiling.

  5. Step 5

    Budgets + monitoring

    Per-route performance budgets wired into CI. Alerts when regressions slip through.

Proof
40%
Median p95 reduction · 8 React/Node engagements

Across recent React/Node frontends with no prior performance work: median p95 latency down 40%, INP down 35%, LCP down 28%. Most wins came from 3-5 changes, not a rewrite.

React performance deep-dive
FAQ

The questions that actually come up.

P95 latency, Core Web Vitals (LCP, INP, CLS), database hotspots, memory leaks, scaling cliffs, deploy slowness, build-time blowups.

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Ready to scope Architecture & Performance?

A 30-minute call. We map your situation against the engagement, give you a real estimate, and tell you honestly whether we are the right team for this.

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